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Content Analysis | A Step-by-Step Guide with Examples

Published on 5 May 2022 by Amy Luo . Revised on 5 December 2022.

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

Books, newspapers, and magazines
Speeches and interviews
Web content and social media posts
Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding). In both types, you categorise or ‘code’ words, themes, and concepts within the texts and then analyse the results.

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyse.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects, or concepts in a set of historical or contemporary texts.

In addition, content analysis can be used to make qualitative inferences by analysing the meaning and semantic relationship of words and concepts.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

Finding correlations and patterns in how concepts are communicated
Understanding the intentions of an individual, group, or institution
Identifying propaganda and bias in communication
Revealing differences in communication in different contexts
Analysing the consequences of communication content, such as the flow of information or audience responses

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Unobtrusive data collection

You can analyse communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

Highly flexible

You can conduct content analysis at any time, in any location, and at low cost. All you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions.

Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct research question .

Next, you follow these five steps.

Step 1: Select the content you will analyse

Based on your research question, choose the texts that you will analyse. You need to decide:

The medium (e.g., newspapers, speeches, or websites) and genre (e.g., opinion pieces, political campaign speeches, or marketing copy)
The criteria for inclusion (e.g., newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
The parameters in terms of date range, location, etc.

If there are only a small number of texts that meet your criteria, you might analyse all of them. If there is a large volume of texts, you can select a sample .

Step 2: Define the units and categories of analysis

Next, you need to determine the level at which you will analyse your chosen texts. This means defining:

The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
The set of categories that you will use for coding. Categories can be objective characteristics (e.g., aged 30–40, lawyer, parent) or more conceptual (e.g., trustworthy, corrupt, conservative, family-oriented).

Step 3: Develop a set of rules for coding

Coding involves organising the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

Step 4: Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti , and Diction , which can help speed up the process of counting and categorising words and phrases.

Step 5: Analyse the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context, and audience of the texts.

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Chapter 17. Content Analysis

Introduction.

Content analysis is a term that is used to mean both a method of data collection and a method of data analysis. Archival and historical works can be the source of content analysis, but so too can the contemporary media coverage of a story, blogs, comment posts, films, cartoons, advertisements, brand packaging, and photographs posted on Instagram or Facebook. Really, almost anything can be the “content” to be analyzed. This is a qualitative research method because the focus is on the meanings and interpretations of that content rather than strictly numerical counts or variables-based causal modeling. [1] Qualitative content analysis (sometimes referred to as QCA) is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest—in other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue. This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis. It is also a nice segue between our data collection methods (e.g., interviewing, observation) chapters and chapters 18 and 19, whose focus is on coding, the primary means of data analysis for most qualitative data. In many ways, the methods of content analysis are quite similar to the method of coding.

content analysis research topic examples

Although the body of material (“content”) to be collected and analyzed can be nearly anything, most qualitative content analysis is applied to forms of human communication (e.g., media posts, news stories, campaign speeches, advertising jingles). The point of the analysis is to understand this communication, to systematically and rigorously explore its meanings, assumptions, themes, and patterns. Historical and archival sources may be the subject of content analysis, but there are other ways to analyze (“code”) this data when not overly concerned with the communicative aspect (see chapters 18 and 19). This is why we tend to consider content analysis its own method of data collection as well as a method of data analysis. Still, many of the techniques you learn in this chapter will be helpful to any “coding” scheme you develop for other kinds of qualitative data. Just remember that content analysis is a particular form with distinct aims and goals and traditions.

An Overview of the Content Analysis Process

The first step: selecting content.

Figure 17.2 is a display of possible content for content analysis. The first step in content analysis is making smart decisions about what content you will want to analyze and to clearly connect this content to your research question or general focus of research. Why are you interested in the messages conveyed in this particular content? What will the identification of patterns here help you understand? Content analysis can be fun to do, but in order to make it research, you need to fit it into a research plan.

Figure 17.1. A Non-exhaustive List of "Content" for Content Analysis

To take one example, let us imagine you are interested in gender presentations in society and how presentations of gender have changed over time. There are various forms of content out there that might help you document changes. You could, for example, begin by creating a list of magazines that are coded as being for “women” (e.g., Women’s Daily Journal ) and magazines that are coded as being for “men” (e.g., Men’s Health ). You could then select a date range that is relevant to your research question (e.g., 1950s–1970s) and collect magazines from that era. You might create a “sample” by deciding to look at three issues for each year in the date range and a systematic plan for what to look at in those issues (e.g., advertisements? Cartoons? Titles of articles? Whole articles?). You are not just going to look at some magazines willy-nilly. That would not be systematic enough to allow anyone to replicate or check your findings later on. Once you have a clear plan of what content is of interest to you and what you will be looking at, you can begin, creating a record of everything you are including as your content. This might mean a list of each advertisement you look at or each title of stories in those magazines along with its publication date. You may decide to have multiple “content” in your research plan. For each content, you want a clear plan for collecting, sampling, and documenting.

The Second Step: Collecting and Storing

Once you have a plan, you are ready to collect your data. This may entail downloading from the internet, creating a Word document or PDF of each article or picture, and storing these in a folder designated by the source and date (e.g., “ Men’s Health advertisements, 1950s”). Sølvberg ( 2021 ), for example, collected posted job advertisements for three kinds of elite jobs (economic, cultural, professional) in Sweden. But collecting might also mean going out and taking photographs yourself, as in the case of graffiti, street signs, or even what people are wearing. Chaise LaDousa, an anthropologist and linguist, took photos of “house signs,” which are signs, often creative and sometimes offensive, hung by college students living in communal off-campus houses. These signs were a focal point of college culture, sending messages about the values of the students living in them. Some of the names will give you an idea: “Boot ’n Rally,” “The Plantation,” “Crib of the Rib.” The students might find these signs funny and benign, but LaDousa ( 2011 ) argued convincingly that they also reproduced racial and gender inequalities. The data here already existed—they were big signs on houses—but the researcher had to collect the data by taking photographs.

In some cases, your content will be in physical form but not amenable to photographing, as in the case of films or unwieldy physical artifacts you find in the archives (e.g., undigitized meeting minutes or scrapbooks). In this case, you need to create some kind of detailed log (fieldnotes even) of the content that you can reference. In the case of films, this might mean watching the film and writing down details for key scenes that become your data. [2] For scrapbooks, it might mean taking notes on what you are seeing, quoting key passages, describing colors or presentation style. As you might imagine, this can take a lot of time. Be sure you budget this time into your research plan.

Researcher Note

A note on data scraping : Data scraping, sometimes known as screen scraping or frame grabbing, is a way of extracting data generated by another program, as when a scraping tool grabs information from a website. This may help you collect data that is on the internet, but you need to be ethical in how to employ the scraper. A student once helped me scrape thousands of stories from the Time magazine archives at once (although it took several hours for the scraping process to complete). These stories were freely available, so the scraping process simply sped up the laborious process of copying each article of interest and saving it to my research folder. Scraping tools can sometimes be used to circumvent paywalls. Be careful here!

The Third Step: Analysis

There is often an assumption among novice researchers that once you have collected your data, you are ready to write about what you have found. Actually, you haven’t yet found anything, and if you try to write up your results, you will probably be staring sadly at a blank page. Between the collection and the writing comes the difficult task of systematically and repeatedly reviewing the data in search of patterns and themes that will help you interpret the data, particularly its communicative aspect (e.g., What is it that is being communicated here, with these “house signs” or in the pages of Men’s Health ?).

The first time you go through the data, keep an open mind on what you are seeing (or hearing), and take notes about your observations that link up to your research question. In the beginning, it can be difficult to know what is relevant and what is extraneous. Sometimes, your research question changes based on what emerges from the data. Use the first round of review to consider this possibility, but then commit yourself to following a particular focus or path. If you are looking at how gender gets made or re-created, don’t follow the white rabbit down a hole about environmental injustice unless you decide that this really should be the focus of your study or that issues of environmental injustice are linked to gender presentation. In the second round of review, be very clear about emerging themes and patterns. Create codes (more on these in chapters 18 and 19) that will help you simplify what you are noticing. For example, “men as outdoorsy” might be a common trope you see in advertisements. Whenever you see this, mark the passage or picture. In your third (or fourth or fifth) round of review, begin to link up the tropes you’ve identified, looking for particular patterns and assumptions. You’ve drilled down to the details, and now you are building back up to figure out what they all mean. Start thinking about theory—either theories you have read about and are using as a frame of your study (e.g., gender as performance theory) or theories you are building yourself, as in the Grounded Theory tradition. Once you have a good idea of what is being communicated and how, go back to the data at least one more time to look for disconfirming evidence. Maybe you thought “men as outdoorsy” was of importance, but when you look hard, you note that women are presented as outdoorsy just as often. You just hadn’t paid attention. It is very important, as any kind of researcher but particularly as a qualitative researcher, to test yourself and your emerging interpretations in this way.

The Fourth and Final Step: The Write-Up

Only after you have fully completed analysis, with its many rounds of review and analysis, will you be able to write about what you found. The interpretation exists not in the data but in your analysis of the data. Before writing your results, you will want to very clearly describe how you chose the data here and all the possible limitations of this data (e.g., historical-trace problem or power problem; see chapter 16). Acknowledge any limitations of your sample. Describe the audience for the content, and discuss the implications of this. Once you have done all of this, you can put forth your interpretation of the communication of the content, linking to theory where doing so would help your readers understand your findings and what they mean more generally for our understanding of how the social world works. [3]

Analyzing Content: Helpful Hints and Pointers

Although every data set is unique and each researcher will have a different and unique research question to address with that data set, there are some common practices and conventions. When reviewing your data, what do you look at exactly? How will you know if you have seen a pattern? How do you note or mark your data?

Let’s start with the last question first. If your data is stored digitally, there are various ways you can highlight or mark up passages. You can, of course, do this with literal highlighters, pens, and pencils if you have print copies. But there are also qualitative software programs to help you store the data, retrieve the data, and mark the data. This can simplify the process, although it cannot do the work of analysis for you.

Qualitative software can be very expensive, so the first thing to do is to find out if your institution (or program) has a universal license its students can use. If they do not, most programs have special student licenses that are less expensive. The two most used programs at this moment are probably ATLAS.ti and NVivo. Both can cost more than $500 [4] but provide everything you could possibly need for storing data, content analysis, and coding. They also have a lot of customer support, and you can find many official and unofficial tutorials on how to use the programs’ features on the web. Dedoose, created by academic researchers at UCLA, is a decent program that lacks many of the bells and whistles of the two big programs. Instead of paying all at once, you pay monthly, as you use the program. The monthly fee is relatively affordable (less than $15), so this might be a good option for a small project. HyperRESEARCH is another basic program created by academic researchers, and it is free for small projects (those that have limited cases and material to import). You can pay a monthly fee if your project expands past the free limits. I have personally used all four of these programs, and they each have their pluses and minuses.

Regardless of which program you choose, you should know that none of them will actually do the hard work of analysis for you. They are incredibly useful for helping you store and organize your data, and they provide abundant tools for marking, comparing, and coding your data so you can make sense of it. But making sense of it will always be your job alone.

So let’s say you have some software, and you have uploaded all of your content into the program: video clips, photographs, transcripts of news stories, articles from magazines, even digital copies of college scrapbooks. Now what do you do? What are you looking for? How do you see a pattern? The answers to these questions will depend partially on the particular research question you have, or at least the motivation behind your research. Let’s go back to the idea of looking at gender presentations in magazines from the 1950s to the 1970s. Here are some things you can look at and code in the content: (1) actions and behaviors, (2) events or conditions, (3) activities, (4) strategies and tactics, (5) states or general conditions, (6) meanings or symbols, (7) relationships/interactions, (8) consequences, and (9) settings. Table 17.1 lists these with examples from our gender presentation study.

Table 17.1. Examples of What to Note During Content Analysis

One thing to note about the examples in table 17.1: sometimes we note (mark, record, code) a single example, while other times, as in “settings,” we are recording a recurrent pattern. To help you spot patterns, it is useful to mark every setting, including a notation on gender. Using software can help you do this efficiently. You can then call up “setting by gender” and note this emerging pattern. There’s an element of counting here, which we normally think of as quantitative data analysis, but we are using the count to identify a pattern that will be used to help us interpret the communication. Content analyses often include counting as part of the interpretive (qualitative) process.

In your own study, you may not need or want to look at all of the elements listed in table 17.1. Even in our imagined example, some are more useful than others. For example, “strategies and tactics” is a bit of a stretch here. In studies that are looking specifically at, say, policy implementation or social movements, this category will prove much more salient.

Another way to think about “what to look at” is to consider aspects of your content in terms of units of analysis. You can drill down to the specific words used (e.g., the adjectives commonly used to describe “men” and “women” in your magazine sample) or move up to the more abstract level of concepts used (e.g., the idea that men are more rational than women). Counting for the purpose of identifying patterns is particularly useful here. How many times is that idea of women’s irrationality communicated? How is it is communicated (in comic strips, fictional stories, editorials, etc.)? Does the incidence of the concept change over time? Perhaps the “irrational woman” was everywhere in the 1950s, but by the 1970s, it is no longer showing up in stories and comics. By tracing its usage and prevalence over time, you might come up with a theory or story about gender presentation during the period. Table 17.2 provides more examples of using different units of analysis for this work along with suggestions for effective use.

Table 17.2. Examples of Unit of Analysis in Content Analysis

Every qualitative content analysis is unique in its particular focus and particular data used, so there is no single correct way to approach analysis. You should have a better idea, however, of what kinds of things to look for and what to look for. The next two chapters will take you further into the coding process, the primary analytical tool for qualitative research in general.

Using Content Analysis

This guide provides an introduction to content analysis, a research methodology that examines words or phrases within a wide range of texts.

Introduction to Content Analysis : Read about the history and uses of content analysis.
Conceptual Analysis : Read an overview of conceptual analysis and its associated methodology.
Relational Analysis : Read an overview of relational analysis and its associated methodology.
Commentary : Read about issues of reliability and validity with regard to content analysis as well as the advantages and disadvantages of using content analysis as a research methodology.
Examples : View examples of real and hypothetical studies that use content analysis.
Annotated Bibliography : Complete list of resources used in this guide and beyond.

An Introduction to Content Analysis

Content analysis is a research tool used to determine the presence of certain words or concepts within texts or sets of texts. Researchers quantify and analyze the presence, meanings and relationships of such words and concepts, then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of which these are a part. Texts can be defined broadly as books, book chapters, essays, interviews, discussions, newspaper headlines and articles, historical documents, speeches, conversations, advertising, theater, informal conversation, or really any occurrence of communicative language. Texts in a single study may also represent a variety of different types of occurrences, such as Palmquist's 1990 study of two composition classes, in which he analyzed student and teacher interviews, writing journals, classroom discussions and lectures, and out-of-class interaction sheets. To conduct a content analysis on any such text, the text is coded, or broken down, into manageable categories on a variety of levels--word, word sense, phrase, sentence, or theme--and then examined using one of content analysis' basic methods: conceptual analysis or relational analysis.

A Brief History of Content Analysis

Historically, content analysis was a time consuming process. Analysis was done manually, or slow mainframe computers were used to analyze punch cards containing data punched in by human coders. Single studies could employ thousands of these cards. Human error and time constraints made this method impractical for large texts. However, despite its impracticality, content analysis was already an often utilized research method by the 1940's. Although initially limited to studies that examined texts for the frequency of the occurrence of identified terms (word counts), by the mid-1950's researchers were already starting to consider the need for more sophisticated methods of analysis, focusing on concepts rather than simply words, and on semantic relationships rather than just presence (de Sola Pool 1959). While both traditions still continue today, content analysis now is also utilized to explore mental models, and their linguistic, affective, cognitive, social, cultural and historical significance.

Uses of Content Analysis

Perhaps due to the fact that it can be applied to examine any piece of writing or occurrence of recorded communication, content analysis is currently used in a dizzying array of fields, ranging from marketing and media studies, to literature and rhetoric, ethnography and cultural studies, gender and age issues, sociology and political science, psychology and cognitive science, and many other fields of inquiry. Additionally, content analysis reflects a close relationship with socio- and psycholinguistics, and is playing an integral role in the development of artificial intelligence. The following list (adapted from Berelson, 1952) offers more possibilities for the uses of content analysis:

Reveal international differences in communication content
Detect the existence of propaganda
Identify the intentions, focus or communication trends of an individual, group or institution
Describe attitudinal and behavioral responses to communications
Determine psychological or emotional state of persons or groups

Types of Content Analysis

In this guide, we discuss two general categories of content analysis: conceptual analysis and relational analysis. Conceptual analysis can be thought of as establishing the existence and frequency of concepts most often represented by words of phrases in a text. For instance, say you have a hunch that your favorite poet often writes about hunger. With conceptual analysis you can determine how many times words such as hunger, hungry, famished, or starving appear in a volume of poems. In contrast, relational analysis goes one step further by examining the relationships among concepts in a text. Returning to the hunger example, with relational analysis, you could identify what other words or phrases hunger or famished appear next to and then determine what different meanings emerge as a result of these groupings.

Conceptual Analysis

Traditionally, content analysis has most often been thought of in terms of conceptual analysis. In conceptual analysis, a concept is chosen for examination, and the analysis involves quantifying and tallying its presence. Also known as thematic analysis [although this term is somewhat problematic, given its varied definitions in current literature--see Palmquist, Carley, & Dale (1997) vis-a-vis Smith (1992)], the focus here is on looking at the occurrence of selected terms within a text or texts, although the terms may be implicit as well as explicit. While explicit terms obviously are easy to identify, coding for implicit terms and deciding their level of implication is complicated by the need to base judgments on a somewhat subjective system. To attempt to limit the subjectivity, then (as well as to limit problems of reliability and validity ), coding such implicit terms usually involves the use of either a specialized dictionary or contextual translation rules. And sometimes, both tools are used--a trend reflected in recent versions of the Harvard and Lasswell dictionaries.

Methods of Conceptual Analysis

Conceptual analysis begins with identifying research questions and choosing a sample or samples. Once chosen, the text must be coded into manageable content categories. The process of coding is basically one of selective reduction . By reducing the text to categories consisting of a word, set of words or phrases, the researcher can focus on, and code for, specific words or patterns that are indicative of the research question.

An example of a conceptual analysis would be to examine several Clinton speeches on health care, made during the 1992 presidential campaign, and code them for the existence of certain words. In looking at these speeches, the research question might involve examining the number of positive words used to describe Clinton's proposed plan, and the number of negative words used to describe the current status of health care in America. The researcher would be interested only in quantifying these words, not in examining how they are related, which is a function of relational analysis. In conceptual analysis, the researcher simply wants to examine presence with respect to his/her research question, i.e. is there a stronger presence of positive or negative words used with respect to proposed or current health care plans, respectively.

Once the research question has been established, the researcher must make his/her coding choices with respect to the eight category coding steps indicated by Carley (1992).

Steps for Conducting Conceptual Analysis

The following discussion of steps that can be followed to code a text or set of texts during conceptual analysis use campaign speeches made by Bill Clinton during the 1992 presidential campaign as an example. To read about each step, click on the items in the list below:

Decide the level of analysis.

First, the researcher must decide upon the level of analysis . With the health care speeches, to continue the example, the researcher must decide whether to code for a single word, such as "inexpensive," or for sets of words or phrases, such as "coverage for everyone."

Decide how many concepts to code for.

The researcher must now decide how many different concepts to code for. This involves developing a pre-defined or interactive set of concepts and categories. The researcher must decide whether or not to code for every single positive or negative word that appears, or only certain ones that the researcher determines are most relevant to health care. Then, with this pre-defined number set, the researcher has to determine how much flexibility he/she allows him/herself when coding. The question of whether the researcher codes only from this pre-defined set, or allows him/herself to add relevant categories not included in the set as he/she finds them in the text, must be answered. Determining a certain number and set of concepts allows a researcher to examine a text for very specific things, keeping him/her on task. But introducing a level of coding flexibility allows new, important material to be incorporated into the coding process that could have significant bearings on one's results.

Decide whether to code for existence or frequency of a concept.

After a certain number and set of concepts are chosen for coding , the researcher must answer a key question: is he/she going to code for existence or frequency ? This is important, because it changes the coding process. When coding for existence, "inexpensive" would only be counted once, no matter how many times it appeared. This would be a very basic coding process and would give the researcher a very limited perspective of the text. However, the number of times "inexpensive" appears in a text might be more indicative of importance. Knowing that "inexpensive" appeared 50 times, for example, compared to 15 appearances of "coverage for everyone," might lead a researcher to interpret that Clinton is trying to sell his health care plan based more on economic benefits, not comprehensive coverage. Knowing that "inexpensive" appeared, but not that it appeared 50 times, would not allow the researcher to make this interpretation, regardless of whether it is valid or not.

Decide on how you will distinguish among concepts.

The researcher must next decide on the , i.e. whether concepts are to be coded exactly as they appear, or if they can be recorded as the same even when they appear in different forms. For example, "expensive" might also appear as "expensiveness." The research needs to determine if the two words mean radically different things to him/her, or if they are similar enough that they can be coded as being the same thing, i.e. "expensive words." In line with this, is the need to determine the level of implication one is going to allow. This entails more than subtle differences in tense or spelling, as with "expensive" and "expensiveness." Determining the level of implication would allow the researcher to code not only for the word "expensive," but also for words that imply "expensive." This could perhaps include technical words, jargon, or political euphemism, such as "economically challenging," that the researcher decides does not merit a separate category, but is better represented under the category "expensive," due to its implicit meaning of "expensive."

Develop rules for coding your texts.

After taking the generalization of concepts into consideration, a researcher will want to create translation rules that will allow him/her to streamline and organize the coding process so that he/she is coding for exactly what he/she wants to code for. Developing a set of rules helps the researcher insure that he/she is coding things consistently throughout the text, in the same way every time. If a researcher coded "economically challenging" as a separate category from "expensive" in one paragraph, then coded it under the umbrella of "expensive" when it occurred in the next paragraph, his/her data would be invalid. The interpretations drawn from that data will subsequently be invalid as well. Translation rules protect against this and give the coding process a crucial level of consistency and coherence.

Decide what to do with "irrelevant" information.

The next choice a researcher must make involves irrelevant information . The researcher must decide whether irrelevant information should be ignored (as Weber, 1990, suggests), or used to reexamine and/or alter the coding scheme. In the case of this example, words like "and" and "the," as they appear by themselves, would be ignored. They add nothing to the quantification of words like "inexpensive" and "expensive" and can be disregarded without impacting the outcome of the coding.

Code the texts.

Once these choices about irrelevant information are made, the next step is to code the text. This is done either by hand, i.e. reading through the text and manually writing down concept occurrences, or through the use of various computer programs. Coding with a computer is one of contemporary conceptual analysis' greatest assets. By inputting one's categories, content analysis programs can easily automate the coding process and examine huge amounts of data, and a wider range of texts, quickly and efficiently. But automation is very dependent on the researcher's preparation and category construction. When coding is done manually, a researcher can recognize errors far more easily. A computer is only a tool and can only code based on the information it is given. This problem is most apparent when coding for implicit information, where category preparation is essential for accurate coding.

Analyze your results.

Once the coding is done, the researcher examines the data and attempts to draw whatever conclusions and generalizations are possible. Of course, before these can be drawn, the researcher must decide what to do with the information in the text that is not coded. One's options include either deleting or skipping over unwanted material, or viewing all information as relevant and important and using it to reexamine, reassess and perhaps even alter one's coding scheme. Furthermore, given that the conceptual analyst is dealing only with quantitative data, the levels of interpretation and generalizability are very limited. The researcher can only extrapolate as far as the data will allow. But it is possible to see trends, for example, that are indicative of much larger ideas. Using the example from step three, if the concept "inexpensive" appears 50 times, compared to 15 appearances of "coverage for everyone," then the researcher can pretty safely extrapolate that there does appear to be a greater emphasis on the economics of the health care plan, as opposed to its universal coverage for all Americans. It must be kept in mind that conceptual analysis, while extremely useful and effective for providing this type of information when done right, is limited by its focus and the quantitative nature of its examination. To more fully explore the relationships that exist between these concepts, one must turn to relational analysis.

Relational Analysis

Relational analysis, like conceptual analysis, begins with the act of identifying concepts present in a given text or set of texts. However, relational analysis seeks to go beyond presence by exploring the relationships between the concepts identified. Relational analysis has also been termed semantic analysis (Palmquist, Carley, & Dale, 1997). In other words, the focus of relational analysis is to look for semantic, or meaningful, relationships. Individual concepts, in and of themselves, are viewed as having no inherent meaning. Rather, meaning is a product of the relationships among concepts in a text. Carley (1992) asserts that concepts are "ideational kernels;" these kernels can be thought of as symbols which acquire meaning through their connections to other symbols.

Theoretical Influences on Relational Analysis

The kind of analysis that researchers employ will vary significantly according to their theoretical approach. Key theoretical approaches that inform content analysis include linguistics and cognitive science.

Linguistic approaches to content analysis focus analysis of texts on the level of a linguistic unit, typically single clause units. One example of this type of research is Gottschalk (1975), who developed an automated procedure which analyzes each clause in a text and assigns it a numerical score based on several emotional/psychological scales. Another technique is to code a text grammatically into clauses and parts of speech to establish a matrix representation (Carley, 1990).

Approaches that derive from cognitive science include the creation of decision maps and mental models. Decision maps attempt to represent the relationship(s) between ideas, beliefs, attitudes, and information available to an author when making a decision within a text. These relationships can be represented as logical, inferential, causal, sequential, and mathematical relationships. Typically, two of these links are compared in a single study, and are analyzed as networks. For example, Heise (1987) used logical and sequential links to examine symbolic interaction. This methodology is thought of as a more generalized cognitive mapping technique, rather than the more specific mental models approach.

Mental models are groups or networks of interrelated concepts that are thought to reflect conscious or subconscious perceptions of reality. According to cognitive scientists, internal mental structures are created as people draw inferences and gather information about the world. Mental models are a more specific approach to mapping because beyond extraction and comparison because they can be numerically and graphically analyzed. Such models rely heavily on the use of computers to help analyze and construct mapping representations. Typically, studies based on this approach follow five general steps:

Identifing concepts
Defining relationship types
Coding the text on the basis of 1 and 2
Coding the statements
Graphically displaying and numerically analyzing the resulting maps

To create the model, a researcher converts a text into a map of concepts and relations; the map is then analyzed on the level of concepts and statements, where a statement consists of two concepts and their relationship. Carley (1990) asserts that this makes possible the comparison of a wide variety of maps, representing multiple sources, implicit and explicit information, as well as socially shared cognitions.

Relational Analysis: Overview of Methods

As with other sorts of inquiry, initial choices with regard to what is being studied and/or coded for often determine the possibilities of that particular study. For relational analysis, it is important to first decide which concept type(s) will be explored in the analysis. Studies have been conducted with as few as one and as many as 500 concept categories. Obviously, too many categories may obscure your results and too few can lead to unreliable and potentially invalid conclusions. Therefore, it is important to allow the context and necessities of your research to guide your coding procedures.

The steps to relational analysis that we consider in this guide suggest some of the possible avenues available to a researcher doing content analysis. We provide an example to make the process easier to grasp. However, the choices made within the context of the example are but only a few of many possibilities. The diversity of techniques available suggests that there is quite a bit of enthusiasm for this mode of research. Once a procedure is rigorously tested, it can be applied and compared across populations over time. The process of relational analysis has achieved a high degree of computer automation but still is, like most forms of research, time consuming. Perhaps the strongest claim that can be made is that it maintains a high degree of statistical rigor without losing the richness of detail apparent in even more qualitative methods.

Three Subcategories of Relational Analysis

Affect extraction: This approach provides an emotional evaluation of concepts explicit in a text. It is problematic because emotion may vary across time and populations. Nevertheless, when extended it can be a potent means of exploring the emotional/psychological state of the speaker and/or writer. Gottschalk (1995) provides an example of this type of analysis. By assigning concepts identified a numeric value on corresponding emotional/psychological scales that can then be statistically examined, Gottschalk claims that the emotional/psychological state of the speaker or writer can be ascertained via their verbal behavior.

Proximity analysis: This approach, on the other hand, is concerned with the co-occurrence of explicit concepts in the text. In this procedure, the text is defined as a string of words. A given length of words, called a window , is determined. The window is then scanned across a text to check for the co-occurrence of concepts. The result is the creation of a concept determined by the concept matrix . In other words, a matrix, or a group of interrelated, co-occurring concepts, might suggest a certain overall meaning. The technique is problematic because the window records only explicit concepts and treats meaning as proximal co-occurrence. Other techniques such as clustering, grouping, and scaling are also useful in proximity analysis.

Cognitive mapping: This approach is one that allows for further analysis of the results from the two previous approaches. It attempts to take the above processes one step further by representing these relationships visually for comparison. Whereas affective and proximal analysis function primarily within the preserved order of the text, cognitive mapping attempts to create a model of the overall meaning of the text. This can be represented as a graphic map that represents the relationships between concepts.

In this manner, cognitive mapping lends itself to the comparison of semantic connections across texts. This is known as map analysis which allows for comparisons to explore "how meanings and definitions shift across people and time" (Palmquist, Carley, & Dale, 1997). Maps can depict a variety of different mental models (such as that of the text, the writer/speaker, or the social group/period), according to the focus of the researcher. This variety is indicative of the theoretical assumptions that support mapping: mental models are representations of interrelated concepts that reflect conscious or subconscious perceptions of reality; language is the key to understanding these models; and these models can be represented as networks (Carley, 1990). Given these assumptions, it's not surprising to see how closely this technique reflects the cognitive concerns of socio-and psycholinguistics, and lends itself to the development of artificial intelligence models.

Steps for Conducting Relational Analysis

The following discussion of the steps (or, perhaps more accurately, strategies) that can be followed to code a text or set of texts during relational analysis. These explanations are accompanied by examples of relational analysis possibilities for statements made by Bill Clinton during the 1998 hearings.

Identify the Question.

The question is important because it indicates where you are headed and why. Without a focused question, the concept types and options open to interpretation are limitless and therefore the analysis difficult to complete. Possibilities for the Hairy Hearings of 1998 might be:

What did Bill Clinton say in the speech? OR What concrete information did he present to the public?

Choose a sample or samples for analysis.

Once the question has been identified, the researcher must select sections of text/speech from the hearings in which Bill Clinton may have not told the entire truth or is obviously holding back information. For relational content analysis, the primary consideration is how much information to preserve for analysis. One must be careful not to limit the results by doing so, but the researcher must also take special care not to take on so much that the coding process becomes too heavy and extensive to supply worthwhile results.

Determine the type of analysis.

Once the sample has been chosen for analysis, it is necessary to determine what type or types of relationships you would like to examine. There are different subcategories of relational analysis that can be used to examine the relationships in texts.

In this example, we will use proximity analysis because it is concerned with the co-occurrence of explicit concepts in the text. In this instance, we are not particularly interested in affect extraction because we are trying to get to the hard facts of what exactly was said rather than determining the emotional considerations of speaker and receivers surrounding the speech which may be unrecoverable.

Once the subcategory of analysis is chosen, the selected text must be reviewed to determine the level of analysis. The researcher must decide whether to code for a single word, such as "perhaps," or for sets of words or phrases like "I may have forgotten."

Reduce the text to categories and code for words or patterns.

At the simplest level, a researcher can code merely for existence. This is not to say that simplicity of procedure leads to simplistic results. Many studies have successfully employed this strategy. For example, Palmquist (1990) did not attempt to establish the relationships among concept terms in the classrooms he studied; his study did, however, look at the change in the presence of concepts over the course of the semester, comparing a map analysis from the beginning of the semester to one constructed at the end. On the other hand, the requirement of one's specific research question may necessitate deeper levels of coding to preserve greater detail for analysis.

In relation to our extended example, the researcher might code for how often Bill Clinton used words that were ambiguous, held double meanings, or left an opening for change or "re-evaluation." The researcher might also choose to code for what words he used that have such an ambiguous nature in relation to the importance of the information directly related to those words.

Explore the relationships between concepts (Strength, Sign & Direction).

Once words are coded, the text can be analyzed for the relationships among the concepts set forth. There are three concepts which play a central role in exploring the relations among concepts in content analysis.

Strength of Relationship: Refers to the degree to which two or more concepts are related. These relationships are easiest to analyze, compare, and graph when all relationships between concepts are considered to be equal. However, assigning strength to relationships retains a greater degree of the detail found in the original text. Identifying strength of a relationship is key when determining whether or not words like unless, perhaps, or maybe are related to a particular section of text, phrase, or idea.
Sign of a Relationship: Refers to whether or not the concepts are positively or negatively related. To illustrate, the concept "bear" is negatively related to the concept "stock market" in the same sense as the concept "bull" is positively related. Thus "it's a bear market" could be coded to show a negative relationship between "bear" and "market". Another approach to coding for strength entails the creation of separate categories for binary oppositions. The above example emphasizes "bull" as the negation of "bear," but could be coded as being two separate categories, one positive and one negative. There has been little research to determine the benefits and liabilities of these differing strategies. Use of Sign coding for relationships in regard to the hearings my be to find out whether or not the words under observation or in question were used adversely or in favor of the concepts (this is tricky, but important to establishing meaning).
Direction of the Relationship: Refers to the type of relationship categories exhibit. Coding for this sort of information can be useful in establishing, for example, the impact of new information in a decision making process. Various types of directional relationships include, "X implies Y," "X occurs before Y" and "if X then Y," or quite simply the decision whether concept X is the "prime mover" of Y or vice versa. In the case of the 1998 hearings, the researcher might note that, "maybe implies doubt," "perhaps occurs before statements of clarification," and "if possibly exists, then there is room for Clinton to change his stance." In some cases, concepts can be said to be bi-directional, or having equal influence. This is equivalent to ignoring directionality. Both approaches are useful, but differ in focus. Coding all categories as bi-directional is most useful for exploratory studies where pre-coding may influence results, and is also most easily automated, or computer coded.
Code the relationships.

One of the main differences between conceptual analysis and relational analysis is that the statements or relationships between concepts are coded. At this point, to continue our extended example, it is important to take special care with assigning value to the relationships in an effort to determine whether the ambiguous words in Bill Clinton's speech are just fillers, or hold information about the statements he is making.

Perform Statisical Analyses.

This step involves conducting statistical analyses of the data you've coded during your relational analysis. This may involve exploring for differences or looking for relationships among the variables you've identified in your study.

Map out the Representations.

In addition to statistical analysis, relational analysis often leads to viewing the representations of the concepts and their associations in a text (or across texts) in a graphical -- or map -- form. Relational analysis is also informed by a variety of different theoretical approaches: linguistic content analysis, decision mapping, and mental models.

The authors of this guide have created the following commentaries on content analysis.

Issues of Reliability & Validity

The issues of reliability and validity are concurrent with those addressed in other research methods. The reliability of a content analysis study refers to its stability , or the tendency for coders to consistently re-code the same data in the same way over a period of time; reproducibility , or the tendency for a group of coders to classify categories membership in the same way; and accuracy , or the extent to which the classification of a text corresponds to a standard or norm statistically. Gottschalk (1995) points out that the issue of reliability may be further complicated by the inescapably human nature of researchers. For this reason, he suggests that coding errors can only be minimized, and not eliminated (he shoots for 80% as an acceptable margin for reliability).

On the other hand, the validity of a content analysis study refers to the correspondence of the categories to the conclusions , and the generalizability of results to a theory.

The validity of categories in implicit concept analysis, in particular, is achieved by utilizing multiple classifiers to arrive at an agreed upon definition of the category. For example, a content analysis study might measure the occurrence of the concept category "communist" in presidential inaugural speeches. Using multiple classifiers, the concept category can be broadened to include synonyms such as "red," "Soviet threat," "pinkos," "godless infidels" and "Marxist sympathizers." "Communist" is held to be the explicit variable, while "red," etc. are the implicit variables.

The overarching problem of concept analysis research is the challenge-able nature of conclusions reached by its inferential procedures. The question lies in what level of implication is allowable, i.e. do the conclusions follow from the data or are they explainable due to some other phenomenon? For occurrence-specific studies, for example, can the second occurrence of a word carry equal weight as the ninety-ninth? Reasonable conclusions can be drawn from substantive amounts of quantitative data, but the question of proof may still remain unanswered.

This problem is again best illustrated when one uses computer programs to conduct word counts. The problem of distinguishing between synonyms and homonyms can completely throw off one's results, invalidating any conclusions one infers from the results. The word "mine," for example, variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. One may obtain an accurate count of that word's occurrence and frequency, but not have an accurate accounting of the meaning inherent in each particular usage. For example, one may find 50 occurrences of the word "mine." But, if one is only looking specifically for "mine" as an explosive device, and 17 of the occurrences are actually personal pronouns, the resulting 50 is an inaccurate result. Any conclusions drawn as a result of that number would render that conclusion invalid.

The generalizability of one's conclusions, then, is very dependent on how one determines concept categories, as well as on how reliable those categories are. It is imperative that one defines categories that accurately measure the idea and/or items one is seeking to measure. Akin to this is the construction of rules. Developing rules that allow one, and others, to categorize and code the same data in the same way over a period of time, referred to as stability , is essential to the success of a conceptual analysis. Reproducibility , not only of specific categories, but of general methods applied to establishing all sets of categories, makes a study, and its subsequent conclusions and results, more sound. A study which does this, i.e. in which the classification of a text corresponds to a standard or norm, is said to have accuracy .

Advantages of Content Analysis

Content analysis offers several advantages to researchers who consider using it. In particular, content analysis:

looks directly at communication via texts or transcripts, and hence gets at the central aspect of social interaction
can allow for both quantitative and qualitative operations
can provides valuable historical/cultural insights over time through analysis of texts
allows a closeness to text which can alternate between specific categories and relationships and also statistically analyzes the coded form of the text
can be used to interpret texts for purposes such as the development of expert systems (since knowledge and rules can both be coded in terms of explicit statements about the relationships among concepts)
is an unobtrusive means of analyzing interactions
provides insight into complex models of human thought and language use

Disadvantages of Content Analysis

Content analysis suffers from several disadvantages, both theoretical and procedural. In particular, content analysis:

can be extremely time consuming
is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation
is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study
is inherently reductive, particularly when dealing with complex texts
tends too often to simply consist of word counts
often disregards the context that produced the text, as well as the state of things after the text is produced
can be difficult to automate or computerize

The Palmquist, Carley and Dale study, a summary of "Applications of Computer-Aided Text Analysis: Analyzing Literary and Non-Literary Texts" (1997) is an example of two studies that have been conducted using both conceptual and relational analysis. The Problematic Text for Content Analysis shows the differences in results obtained by a conceptual and a relational approach to a study.

Related Information: Example of a Problematic Text for Content Analysis

In this example, both students observed a scientist and were asked to write about the experience.

Student A: I found that scientists engage in research in order to make discoveries and generate new ideas. Such research by scientists is hard work and often involves collaboration with other scientists which leads to discoveries which make the scientists famous. Such collaboration may be informal, such as when they share new ideas over lunch, or formal, such as when they are co-authors of a paper.

Student B: It was hard work to research famous scientists engaged in collaboration and I made many informal discoveries. My research showed that scientists engaged in collaboration with other scientists are co-authors of at least one paper containing their new ideas. Some scientists make formal discoveries and have new ideas.

Content analysis coding for explicit concepts may not reveal any significant differences. For example, the existence of "I, scientist, research, hard work, collaboration, discoveries, new ideas, etc..." are explicit in both texts, occur the same number of times, and have the same emphasis. Relational analysis or cognitive mapping, however, reveals that while all concepts in the text are shared, only five concepts are common to both. Analyzing these statements reveals that Student A reports on what "I" found out about "scientists," and elaborated the notion of "scientists" doing "research." Student B focuses on what "I's" research was and sees scientists as "making discoveries" without emphasis on research.

Related Information: The Palmquist, Carley and Dale Study

Consider these two questions: How has the depiction of robots changed over more than a century's worth of writing? And, do students and writing instructors share the same terms for describing the writing process? Although these questions seem totally unrelated, they do share a commonality: in the Palmquist, Carley & Dale study, their answers rely on computer-aided text analysis to demonstrate how different texts can be analyzed.

Literary texts

One half of the study explored the depiction of robots in 27 science fiction texts written between 1818 and 1988. After texts were divided into three historically defined groups, readers look for how the depiction of robots has changed over time. To do this, researchers had to create concept lists and relationship types, create maps using a computer software (see Fig. 1), modify those maps and then ultimately analyze them. The final product of the analysis revealed that over time authors were less likely to depict robots as metallic humanoids.

Non-literary texts

The second half of the study used student journals and interviews, teacher interviews, texts books, and classroom observations as the non-literary texts from which concepts and words were taken. The purpose behind the study was to determine if, in fact, over time teacher and students would begin to share a similar vocabulary about the writing process. Again, researchers used computer software to assist in the process. This time, computers helped researchers generated a concept list based on frequently occurring words and phrases from all texts. Maps were also created and analyzed in this study (see Fig. 2).

Annotated Bibliography

Resources On How To Conduct Content Analysis

Beard, J., & Yaprak, A. (1989). Language implications for advertising in international markets: A model for message content and message execution. A paper presented at the 8th International Conference on Language Communication for World Business and the Professions. Ann Arbor, MI.

This report discusses the development and testing of a content analysis model for assessing advertising themes and messages aimed primarily at U.S. markets which seeks to overcome barriers in the cultural environment of international markets. Texts were categorized under 3 headings: rational, emotional, and moral. The goal here was to teach students to appreciate differences in language and culture.

Berelson, B. (1971). Content analysis in communication research . New York: Hafner Publishing Company.

While this book provides an extensive outline of the uses of content analysis, it is far more concerned with conveying a critical approach to current literature on the subject. In this respect, it assumes a bit of prior knowledge, but is still accessible through the use of concrete examples.

Budd, R. W., Thorp, R.K., & Donohew, L. (1967). Content analysis of communications . New York: Macmillan Company.

Although published in 1967, the decision of the authors to focus on recent trends in content analysis keeps their insights relevant even to modern audiences. The book focuses on specific uses and methods of content analysis with an emphasis on its potential for researching human behavior. It is also geared toward the beginning researcher and breaks down the process of designing a content analysis study into 6 steps that are outlined in successive chapters. A useful annotated bibliography is included.

Carley, K. (1992). Coding choices for textual analysis: A comparison of content analysis and map analysis. Unpublished Working Paper.

Comparison of the coding choices necessary to conceptual analysis and relational analysis, especially focusing on cognitive maps. Discusses concept coding rules needed for sufficient reliability and validity in a Content Analysis study. In addition, several pitfalls common to texts are discussed.

Carley, K. (1990). Content analysis. In R.E. Asher (Ed.), The Encyclopedia of Language and Linguistics. Edinburgh: Pergamon Press.

Quick, yet detailed, overview of the different methodological kinds of Content Analysis. Carley breaks down her paper into five sections, including: Conceptual Analysis, Procedural Analysis, Relational Analysis, Emotional Analysis and Discussion. Also included is an excellent and comprehensive Content Analysis reference list.

Carley, K. (1989). Computer analysis of qualitative data . Pittsburgh, PA: Carnegie Mellon University.

Presents graphic, illustrated representations of computer based approaches to content analysis.

Carley, K. (1992). MECA . Pittsburgh, PA: Carnegie Mellon University.

A resource guide explaining the fifteen routines that compose the Map Extraction Comparison and Analysis (MECA) software program. Lists the source file, input and out files, and the purpose for each routine.

Carney, T. F. (1972). Content analysis: A technique for systematic inference from communications . Winnipeg, Canada: University of Manitoba Press.

This book introduces and explains in detail the concept and practice of content analysis. Carney defines it; traces its history; discusses how content analysis works and its strengths and weaknesses; and explains through examples and illustrations how one goes about doing a content analysis.

de Sola Pool, I. (1959). Trends in content analysis . Urbana, Ill: University of Illinois Press.

The 1959 collection of papers begins by differentiating quantitative and qualitative approaches to content analysis, and then details facets of its uses in a wide variety of disciplines: from linguistics and folklore to biography and history. Includes a discussion on the selection of relevant methods and representational models.

Duncan, D. F. (1989). Content analysis in health educaton research: An introduction to purposes and methods. Heatlth Education, 20 (7).

This article proposes using content analysis as a research technique in health education. A review of literature relating to applications of this technique and a procedure for content analysis are presented.

Gottschalk, L. A. (1995). Content analysis of verbal behavior: New findings and clinical applications. Hillside, NJ: Lawrence Erlbaum Associates, Inc.

This book primarily focuses on the Gottschalk-Gleser method of content analysis, and its application as a method of measuring psychological dimensions of children and adults via the content and form analysis of their verbal behavior, using the grammatical clause as the basic unit of communication for carrying semantic messages generated by speakers or writers.

Krippendorf, K. (1980). Content analysis: An introduction to its methodology Beverly Hills, CA: Sage Publications.

This is one of the most widely quoted resources in many of the current studies of Content Analysis. Recommended as another good, basic resource, as Krippendorf presents the major issues of Content Analysis in much the same way as Weber (1975).

Moeller, L. G. (1963). An introduction to content analysis--including annotated bibliography . Iowa City: University of Iowa Press.

A good reference for basic content analysis. Discusses the options of sampling, categories, direction, measurement, and the problems of reliability and validity in setting up a content analysis. Perhaps better as a historical text due to its age.

Smith, C. P. (Ed.). (1992). Motivation and personality: Handbook of thematic content analysis. New York: Cambridge University Press.

Billed by its authors as "the first book to be devoted primarily to content analysis systems for assessment of the characteristics of individuals, groups, or historical periods from their verbal materials." The text includes manuals for using various systems, theory, and research regarding the background of systems, as well as practice materials, making the book both a reference and a handbook.

Solomon, M. (1993). Content analysis: a potent tool in the searcher's arsenal. Database, 16 (2), 62-67.

Online databases can be used to analyze data, as well as to simply retrieve it. Online-media-source content analysis represents a potent but little-used tool for the business searcher. Content analysis benchmarks useful to advertisers include prominence, offspin, sponsor affiliation, verbatims, word play, positioning and notational visibility.

Weber, R. P. (1990). Basic content analysis, second edition . Newbury Park, CA: Sage Publications.

Good introduction to Content Analysis. The first chapter presents a quick overview of Content Analysis. The second chapter discusses content classification and interpretation, including sections on reliability, validity, and the creation of coding schemes and categories. Chapter three discusses techniques of Content Analysis, using a number of tables and graphs to illustrate the techniques. Chapter four examines issues in Content Analysis, such as measurement, indication, representation and interpretation.

Examples of Content Analysis

Adams, W., & Shriebman, F. (1978). Television network news: Issues in content research . Washington, DC: George Washington University Press.

A fairly comprehensive application of content analysis to the field of television news reporting. The books tripartite division discusses current trends and problems with news criticism from a content analysis perspective, four different content analysis studies of news media, and makes recommendations for future research in the area. Worth a look by anyone interested in mass communication research.

Auter, P. J., & Moore, R. L. (1993). Buying from a friend: a content analysis of two teleshopping programs. Journalism Quarterly, 70 (2), 425-437.

A preliminary study was conducted to content-analyze random samples of two teleshopping programs, using a measure of content interactivity and a locus of control message index.

Barker, S. P. (???) Fame: A content analysis study of the American film biography. Ohio State University. Thesis.

Barker examined thirty Oscar-nominated films dating from 1929 to 1979 using O.J. Harvey Belief System and the Kohlberg's Moral Stages to determine whether cinema heroes were positive role models for fame and success or morally ambiguous celebrities. Content analysis was successful in determining several trends relative to the frequency and portrayal of women in film, the generally high ethical character of the protagonists, and the dogmatic, close-minded nature of film antagonists.

Bernstein, J. M. & Lacy, S. (1992). Contextual coverage of government by local television news. Journalism Quarterly, 69 (2), 329-341.

This content analysis of 14 local television news operations in five markets looks at how local TV news shows contribute to the marketplace of ideas. Performance was measured as the allocation of stories to types of coverage that provide the context about events and issues confronting the public.

Blaikie, A. (1993). Images of age: a reflexive process. Applied Ergonomics, 24 (1), 51-58.

Content analysis of magazines provides a sharp instrument for reflecting the change in stereotypes of aging over past decades.

Craig, R. S. (1992). The effect of day part on gender portrayals in television commercials: a content analysis. Sex Roles: A Journal of Research, 26 (5-6), 197-213.

Gender portrayals in 2,209 network television commercials were content analyzed. To compare differences between three day parts, the sample was chosen from three time periods: daytime, evening prime time, and weekend afternoon sportscasts. The results indicate large and consistent differences in the way men and women are portrayed in these three day parts, with almost all comparisons reaching significance at the .05 level. Although ads in all day parts tended to portray men in stereotypical roles of authority and dominance, those on weekends tended to emphasize escape form home and family. The findings of earlier studies which did not consider day part differences may now have to be reevaluated.

Dillon, D. R. et al. (1992). Article content and authorship trends in The Reading Teacher, 1948-1991. The Reading Teacher, 45 (5), 362-368.

The authors explore changes in the focus of the journal over time.

Eberhardt, EA. (1991). The rhetorical analysis of three journal articles: The study of form, content, and ideology. Ft. Collins, CO: Colorado State University.

Eberhardt uses content analysis in this thesis paper to analyze three journal articles that reported on President Ronald Reagan's address in which he responded to the Tower Commission report concerning the IranContra Affair. The reports concentrated on three rhetorical elements: idea generation or content; linguistic style or choice of language; and the potential societal effect of both, which Eberhardt analyzes, along with the particular ideological orientation espoused by each magazine.

Ellis, B. G. & Dick, S. J. (1996). 'Who was 'Shadow'? The computer knows: applying grammar-program statistics in content analyses to solve mysteries about authorship. Journalism & Mass Communication Quarterly, 73 (4), 947-963.

This study's objective was to employ the statistics-documentation portion of a word-processing program's grammar-check feature as a final, definitive, and objective tool for content analyses - used in tandem with qualitative analyses - to determine authorship. Investigators concluded there was significant evidence from both modalities to support their theory that Henry Watterson, long-time editor of the Louisville Courier-Journal, probably was the South's famed Civil War correspondent "Shadow" and to rule out another prime suspect, John H. Linebaugh of the Memphis Daily Appeal. Until now, this Civil War mystery has never been conclusively solved, puzzling historians specializing in Confederate journalism.

Gottschalk, L. A., Stein, M. K. & Shapiro, D.H. (1997). The application of computerized content analysis in a psychiatric outpatient clinic. Journal of Clinical Psychology, 53 (5) , 427-442.

Twenty-five new psychiatric outpatients were clinically evaluated and were administered a brief psychological screening battery which included measurements of symptoms, personality, and cognitive function. Included in this assessment procedure were the Gottschalk-Gleser Content Analysis Scales on which scores were derived from five minute speech samples by means of an artificial intelligence-based computer program. The use of this computerized content analysis procedure for initial, rapid diagnostic neuropsychiatric appraisal is supported by this research.

Graham, J. L., Kamins, M. A., & Oetomo, D. S. (1993). Content analysis of German and Japanese advertising in print media from Indonesia, Spain, and the United States. Journal of Advertising , 22 (2), 5-16.

The authors analyze informational and emotional content in print advertisements in order to consider how home-country culture influences firms' marketing strategies and tactics in foreign markets. Research results provided evidence contrary to the original hypothesis that home-country culture would influence ads in each of the target countries.

Herzog, A. (1973). The B.S. Factor: The theory and technique of faking it in America . New York: Simon and Schuster.

Herzog takes a look at the rhetoric of American culture using content analysis to point out discrepancies between intention and reality in American society. The study reveals, albeit in a comedic tone, how double talk and "not quite lies" are pervasive in our culture.

Horton, N. S. (1986). Young adult literature and censorship: A content analysis of seventy-eight young adult books . Denton, TX: North Texas State University.

The purpose of Horton's content analysis was to analyze a representative seventy-eight current young adult books to determine the extent to which they contain items which are objectionable to would-be censors. Seventy-eight books were identified which fit the criteria of popularity and literary quality. Each book was analyzed for, and tallied for occurrence of, six categories, including profanity, sex, violence, parent conflict, drugs and condoned bad behavior.

Isaacs, J. S. (1984). A verbal content analysis of the early memories of psychiatric patients . Berkeley: California School of Professional Psychology.

Isaacs did a content analysis investigation on the relationship between words and phrases used in early memories and clinical diagnosis. His hypothesis was that in conveying their early memories schizophrenic patients tend to use an identifiable set of words and phrases more frequently than do nonpatients and that schizophrenic patients use these words and phrases more frequently than do patients with major affective disorders.

Jean Lee, S. K. & Hwee Hoon, T. (1993). Rhetorical vision of men and women managers in Singapore. Human Relations, 46 (4), 527-542.

A comparison of media portrayal of male and female managers' rhetorical vision in Singapore is made. Content analysis of newspaper articles used to make this comparison also reveals the inherent conflicts that women managers have to face. Purposive and multi-stage sampling of articles are utilized.

Kaur-Kasior, S. (1987). The treatment of culture in greeting cards: A content analysis . Bowling Green, OH: Bowling Green State University.

Using six historical periods dating from 1870 to 1987, this content analysis study attempted to determine what structural/cultural aspects of American society were reflected in greeting cards. The study determined that the size of cards increased over time, included more pages, and had animals and flowers as their most dominant symbols. In addition, white was the most common color used. Due to habituation and specialization, says the author, greeting cards have become institutionalized in American culture.

Koza, J. E. (1992). The missing males and other gender-related issues in music education: A critical analysis of evidence from the Music Supervisor's Journal, 1914-1924. Paper presented at the annual meeting of the American Educational Research Association. San Francisco.

The goal of this study was to identify all educational issues that would today be explicitly gender related and to analyze the explanations past music educators gave for the existence of gender-related problems. A content analysis of every gender-related reference was undertaken, finding that the current preoccupation with males in music education has a long history and that little has changed since the early part of this century.

Laccinole, M. D. (1982). Aging and married couples: A language content analysis of a conversational and expository speech task . Eugene, OR: University of Oregon.

Using content analysis, this paper investigated the relationship of age to the use of the grammatical categories, and described the differences in the usage of these grammatical categories in a conversation and expository speech task by fifty married couples. The subjects Laccinole used in his analysis were Caucasian, English speaking, middle class, ranged in ages from 20 to 83 years of age, were in good health and had no history of communication disorders.

Laffal, J. (1995). A concept analysis of Jonathan Swift's 'A Tale of a Tub' and 'Gulliver's Travels.' Computers and Humanities, 29 (5), 339-362.

In this study, comparisons of concept profiles of "Tub," "Gulliver," and Swift's own contemporary texts, as well as a composite text of 18th century writers, reveal that "Gulliver" is conceptually different from "Tub." The study also discovers that the concepts and words of these texts suggest two strands in Swift's thinking.

Lewis, S. M. (1991). Regulation from a deregulatory FCC: Avoiding discursive dissonance. Masters Thesis, Fort Collins, CO: Colorado State University.

This thesis uses content analysis to examine inconsistent statements made by the Federal Communications Commission (FCC) in its policy documents during the 1980s. Lewis analyzes positions set forth by the FCC in its policy statements and catalogues different strategies that can be used by speakers to be or to appear consistent, as well as strategies to avoid inconsistent speech or discursive dissonance.

Norton, T. L. (1987). The changing image of childhood: A content analysis of Caldecott Award books. Los Angeles: University of South Carolina.

Content analysis was conducted on 48 Caldecott Medal Recipient books dating from 1938 to 1985 to determine whether the reflect the idea that the social perception of childhood has altered since the early 1960's. The results revealed an increasing "loss of childhood innocence," as well as a general sentimentality for childhood pervasive in the texts. Suggests further study of children's literature to confirm the validity of such study.

O'Dell, J. W. & Weideman, D. (1993). Computer content analysis of the Schreber case. Journal of Clinical Psychology, 49 (1), 120-125.

An example of the application of content analysis as a means of recreating a mental model of the psychology of an individual.

Pratt, C. A. & Pratt, C. B. (1995). Comparative content analysis of food and nutrition advertisements in Ebony, Essence, and Ladies' Home Journal. Journal of Nutrition Education, 27 (1), 11-18.

This study used content analysis to measure the frequencies and forms of food, beverage, and nutrition advertisements and their associated health-promotional message in three U.S. consumer magazines during two 3-year periods: 1980-1982 and 1990-1992. The study showed statistically significant differences among the three magazines in both frequencies and types of major promotional messages in the advertisements. Differences between the advertisements in Ebony and Essence, the readerships of which were primarily African-American, and those found in Ladies Home Journal were noted, as were changes in the two time periods. Interesting tie in to ethnographic research studies?

Riffe, D., Lacy, S., & Drager, M. W. (1996). Sample size in content analysis of weekly news magazines. Journalism & Mass Communication Quarterly,73 (3), 635-645.

This study explores a variety of approaches to deciding sample size in analyzing magazine content. Having tested random samples of size six, eight, ten, twelve, fourteen, and sixteen issues, the authors show that a monthly stratified sample of twelve issues is the most efficient method for inferring to a year's issues.

Roberts, S. K. (1987). A content analysis of how male and female protagonists in Newbery Medal and Honor books overcome conflict: Incorporating a locus of control framework. Fayetteville, AR: University of Arkansas.

The purpose of this content analysis was to analyze Newbery Medal and Honor books in order to determine how male and female protagonists were assigned behavioral traits in overcoming conflict as it relates to an internal or external locus of control schema. Roberts used all, instead of just a sample, of the fictional Newbery Medal and Honor books which met his study's criteria. A total of 120 male and female protagonists were categorized, from Newbery books dating from 1922 to 1986.

Schneider, J. (1993). Square One TV content analysis: Final report . New York: Children's Television Workshop.

This report summarizes the mathematical and pedagogical content of the 230 programs in the Square One TV library after five seasons of production, relating that content to the goals of the series which were to make mathematics more accessible, meaningful, and interesting to the children viewers.

Smith, T. E., Sells, S. P., and Clevenger, T. Ethnographic content analysis of couple and therapist perceptions in a reflecting team setting. The Journal of Marital and Family Therapy, 20 (3), 267-286.

An ethnographic content analysis was used to examine couple and therapist perspectives about the use and value of reflecting team practice. Postsession ethnographic interviews from both couples and therapists were examined for the frequency of themes in seven categories that emerged from a previous ethnographic study of reflecting teams. Ethnographic content analysis is briefly contrasted with conventional modes of quantitative content analysis to illustrate its usefulness and rationale for discovering emergent patterns, themes, emphases, and process using both inductive and deductive methods of inquiry.

Stahl, N. A. (1987). Developing college vocabulary: A content analysis of instructional materials. Reading, Research and Instruction , 26 (3).

This study investigates the extent to which the content of 55 college vocabulary texts is consistent with current research and theory on vocabulary instruction. It recommends less reliance on memorization and more emphasis on deep understanding and independent vocabulary development.

Swetz, F. (1992). Fifteenth and sixteenth century arithmetic texts: What can we learn from them? Science and Education, 1 (4).

Surveys the format and content of 15th and 16th century arithmetic textbooks, discussing the types of problems that were most popular in these early texts and briefly analyses problem contents. Notes the residual educational influence of this era's arithmetical and instructional practices.

Walsh, K., et al. (1996). Management in the public sector: a content analysis of journals. Public Administration 74 (2), 315-325.

The popularity and implementaion of managerial ideas from 1980 to 1992 are examined through the content of five journals revolving on local government, health, education and social service. Contents were analyzed according to commercialism, user involvement, performance evaluation, staffing, strategy and involvement with other organizations. Overall, local government showed utmost involvement with commercialism while health and social care articles were most concerned with user involvement.

For Further Reading

Abernethy, A. M., & Franke, G. R. (1996).The information content of advertising: a meta-analysis. Journal of Advertising, Summer 25 (2) , 1-18.

Carley, K., & Palmquist, M. (1992). Extracting, representing and analyzing mental models. Social Forces , 70 (3), 601-636.

Fan, D. (1988). Predictions of public opinion from the mass media: Computer content analysis and mathematical modeling . New York, NY: Greenwood Press.

Franzosi, R. (1990). Computer-assisted coding of textual data: An application to semantic grammars. Sociological Methods and Research, 19 (2), 225-257.

McTavish, D.G., & Pirro, E. (1990) Contextual content analysis. Quality and Quantity , 24 , 245-265.

Palmquist, M. E. (1990). The lexicon of the classroom: language and learning in writing class rooms . Doctoral dissertation, Carnegie Mellon University, Pittsburgh, PA.

Palmquist, M. E., Carley, K.M., and Dale, T.A. (1997). Two applications of automated text analysis: Analyzing literary and non-literary texts. In C. Roberts (Ed.), Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Tanscripts. Hillsdale, NJ: Lawrence Erlbaum Associates.

Roberts, C.W. (1989). Other than counting words: A linguistic approach to content analysis. Social Forces, 68 , 147-177.

Issues in Content Analysis

Jolliffe, L. (1993). Yes! More content analysis! Newspaper Research Journal , 14 (3-4), 93-97.

The author responds to an editorial essay by Barbara Luebke which criticizes excessive use of content analysis in newspaper content studies. The author points out the positive applications of content analysis when it is theory-based and utilized as a means of suggesting how or why the content exists, or what its effects on public attitudes or behaviors may be.

Kang, N., Kara, A., Laskey, H. A., & Seaton, F. B. (1993). A SAS MACRO for calculating intercoder agreement in content analysis. Journal of Advertising, 22 (2), 17-28.

A key issue in content analysis is the level of agreement across the judgments which classify the objects or stimuli of interest. A review of articles published in the Journal of Advertising indicates that many authors are not fully utilizing recommended measures of intercoder agreement and thus may not be adequately establishing the reliability of their research. This paper presents a SAS MACRO which facilitates the computation of frequently recommended indices of intercoder agreement in content analysis.

Lacy, S. & Riffe, D. (1996). Sampling error and selecting intercoder reliability samples for nominal content categories. Journalism & Mass Communication Quarterly, 73 (4) , 693-704.

This study views intercoder reliability as a sampling problem. It develops a formula for generating sample sizes needed to have valid reliability estimates. It also suggests steps for reporting reliability. The resulting sample sizes will permit a known degree of confidence that the agreement in a sample of items is representative of the pattern that would occur if all content items were coded by all coders.

Riffe, D., Aust, C. F., & Lacy, S. R. (1993). The effectiveness of random, consecutive day and constructed week sampling in newspaper content analysis. Journalism Quarterly, 70 (1), 133-139.

This study compares 20 sets each of samples for four different sizes using simple random, constructed week and consecutive day samples of newspaper content. Comparisons of sample efficiency, based on the percentage of sample means in each set of 20 falling within one or two standard errors of the population mean, show the superiority of constructed week sampling.

Thomas, S. (1994). Artifactual study in the analysis of culture: A defense of content analysis in a postmodern age. Communication Research, 21 (6), 683-697.

Although both modern and postmodern scholars have criticized the method of content analysis with allegations of reductionism and other epistemological limitations, it is argued here that these criticisms are ill founded. In building and argument for the validity of content analysis, the general value of artifact or text study is first considered.

Zollars, C. (1994). The perils of periodical indexes: Some problems in constructing samples for content analysis and culture indicators research. Communication Research, 21 (6), 698-714.

The author examines problems in using periodical indexes to construct research samples via the use of content analysis and culture indicator research. Issues of historical and idiosyncratic changes in index subject category heading and subheadings make article headings potentially misleading indicators. Index subject categories are not necessarily invalid as a result; nevertheless, the author discusses the need to test for category longevity, coherence, and consistency over time, and suggests the use of oversampling, cross-references, and other techniques as a means of correcting and/or compensating for hidden inaccuracies in classification, and as a means of constructing purposive samples for analytic comparisons.

Busch, Carol, Paul S. De Maret, Teresa Flynn, Rachel Kellum, Sheri Le, Brad Meyers, Matt Saunders, Robert White, and Mike Palmquist. (2005). Content Analysis. Writing@CSU . Colorado State University. https://writing.colostate.edu/guides/guide.cfm?guideid=61

How it works

What is Content Analysis – Steps & Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

“The content analysis identifies specific words, patterns, concepts, themes, phrases, characters, or sentences within the recorded communication content.”

To conduct content analysis, you need to gather data from multiple sources; it can be anything or any form of data, including text, audio, or videos.

Depending on the requirements of your analysis, you may have to use a primary or secondary form of data , including:

The Purpose of Content Analysis

There are so many objectives of content analysis. Some fundamental objectives are given below.

To simplify the content.
To get a clear, in-depth meaning of the language.
To identify the uses of language.
To know the impact of language on society.
To find out the association of the language with cultures, interpersonal relationships, and communication.
To gain an in-depth understanding of the concept.
To find out the context, behaviour, and response of the speaker.
To analyse the trends and association between the text and multimedia.

When to Use Content Analysis?

There are many uses of the content analysis; some of them are listed below:

The content analysis is used.

To represent the content precisely, breaking it into short form.
To describe the characteristics of the content.
To support an argument.
It is used in many walks of life, including marketing, media, literature, etc.
It is used for extracting essential information from a large amount of data.

Types of Content Analysis

Content analysis is a broad concept, and it has various types depending on various fields. However, people from all walks of life use it at their convenience. Some of the popular methods are given below:

Confused between qualitative and quantitative methods of data analysis? No idea what discourse and content analysis are?

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Advantages and Disadvantages of Content Analysis

Content analysis has so many benefits, which are given below.

Content analysis:

Offers both qualitative and quantitative analysis of the communication.
Provides an in-depth understanding of the content by making it precise.
Enables us to understand the context and perception of the speaker.
Provides insight into complex models of human thoughts and language use.
Provides historical/cultural insight.
It can be applied at any given time, place, and people.
It helps to learn any language, its origin, and association with society and culture

Disadvantages

There are also some disadvantages of using the method of content analysis which are given below:

is very time-consuming.
Cannot interpret a large amount of data accurately and is subjected to increased error.
Cannot be computerised easily.

How to Conduct a Content Analysis?

If you want to conduct the content analysis, so here are some steps that you have to follow for that purpose. Those steps are given below.

Develop a Research Question and Select the Content

It’s essential to have a research question to proceed with your study. After selecting your research question, you need to find out the relevant resources to analyse.

Example: If you want to find out the impact of plagiarism on the credibility of the authors. You can examine the relevant materials available on the topic from the internet, newspapers, and books published during the past 5-10 years.

Could you read it Thoroughly?

At this point, you have to read the content thoroughly until you understand it.

Condensation

It would help if you broke the text into smaller portions for clear interpretation. In short, you have to create categories or smaller text from a large amount of given data.

The unit of analysis is the basic unit of text to be classified. It can be a word, phrase, a theme, a plot, a newspaper article.

Code the Content

It takes a long to go through the textual data. Coding is a way of tagging the data and organising it into a sequence of symbols, numbers, and letters to highlight the relevant points. At this point, you have to draw meanings from those condensed parts. You have to understand the meaning and context of the text and the speaker clearly.

Analyse and Interpret the Data

You can use statistical analysis to analyse the data. It is a method of collecting, analysing, and interpreting ample data to discover underlying patterns and details. Statistics are used in every field to make better decisions. It would help if you aimed to retain the meaning of the content while making it precise.

Frequently Asked Questions

How to perform content analysis.

To perform content analysis:

Define research objectives.
Select a representative sample.
Develop coding categories.
Analyze content systematically.
Apply coding to data.
Interpret results to draw insights about themes, patterns, and meanings.

18.5 Content analysis

Learning objectives.

Learners will be able to…

Explain defining features of content analysis as a strategy for analyzing qualitative data
Determine when content analysis can be most effectively used
Formulate an initial content analysis plan (if appropriate for your research proposal)

What are you trying to accomplish with content analysis

Much like with thematic analysis, if you elect to use content analysis to analyze your qualitative data, you will be deconstructing the artifacts that you have sampled and looking for similarities across these deconstructed parts. Also consistent with thematic analysis, you will be seeking to bring together these similarities in the discussion of your findings to tell a collective story of what you learned across your data. While the distinction between thematic analysis and content analysis is somewhat murky, if you are looking to distinguish between the two, content analysis:

Places greater emphasis on determining the unit of analysis. Just to quickly distinguish, when we discussed sampling in Chapter 10 we also used the term “unit of analysis. As a reminder, when we are talking about sampling, unit of analysis refers to the entity that a researcher wants to say something about at the end of her study (individual, group, or organization). However, for our purposes when we are conducting a content analysis, this term has to do with the ‘chunk’ or segment of data you will be looking at to reflect a particular idea. This may be a line, a paragraph, a section, an image or section of an image, a scene, etc., depending on the type of artifact you are dealing with and the level at which you want to subdivide this artifact.
Content analysis is also more adept at bringing together a variety of forms of artifacts in the same study. While other approaches can certainly accomplish this, content analysis more readily allows the researcher to deconstruct, label and compare different kinds of ‘content’. For example, perhaps you have developed a new advocacy training for community members. To evaluate your training you want to analyze a variety of products they create after the workshop, including written products (e.g. letters to their representatives, community newsletters), audio/visual products (e.g. interviews with leaders, photos hosted in a local art exhibit on the topic) and performance products (e.g. hosting town hall meetings, facilitating rallies). Content analysis can allow you the capacity to examine evidence across these different formats.

For some more in-depth discussion comparing these two approaches, including more philosophical differences between the two, check out this article by Vaismoradi, Turunen, and Bondas (2013) . [1]

Variations in the approach

There are also significant variations among different content analysis approaches. Some of these approaches are more concerned with quantifying (counting) how many times a code representing a specific concept or idea appears. These are more quantitative and deductive in nature. Other approaches look for codes to emerge from the data to help describe some idea or event. These are more qualitative and inductive . Hsieh and Shannon (2005) [2] describe three approaches to help understand some of these differences:

Conventional Content Analysis. Starting with a general idea or phenomenon you want to explore (for which there is limited data), coding categories then emerge from the raw data. These coding categories help us understand the different dimensions, patterns, and trends that may exist within the raw data collected in our research.
Directed Content Analysis. Starts with a theory or existing research for which you develop your initial codes (there is some existing research, but incomplete in some aspects) and uses these to guide your initial analysis of the raw data to flesh out a more detailed understanding of the codes and ultimately, the focus of your study.
Summative Content Analysis. Starts by examining how many times and where codes are showing up in your data, but then looks to develop an understanding or an “interpretation of the underlying context” (p.1277) for how they are being used. As you might have guessed, this approach is more likely to be used if you’re studying a topic that already has some existing research that forms a basic place to begin the analysis.

This is only one system of categorization for different approaches to content analysis. If you are interested in utilizing a content analysis for your proposal, you will want to design an approach that fits well with the aim of your research and will help you generate findings that will help to answer your research question(s). Make sure to keep this as your north star, guiding all aspects of your design.

Determining your codes

We are back to coding! As in thematic analysis, you will be coding your data (labeling smaller chunks of information within each data artifact of your sample). In content analysis, you may be using pre-determined codes, such as those suggested by an existing theory (deductive) or you may seek out emergent codes that you uncover as you begin reviewing your data (inductive). Regardless of which approach you take, you will want to develop a well-documented codebook.

A codebook is a document that outlines the list of codes you are using as you analyze your data, a descriptive definition of each of these codes, and any decision-rules that apply to your codes. A decision-rule provides information on how the researcher determines what code should be placed on an item, especially when codes may be similar in nature. If you are using a deductive approach, your codebook will largely be formed prior to analysis, whereas if you use an inductive approach, your codebook will be built over time. To help illustrate what this might look like, Figure 18.12 offers a brief excerpt of a codebook from one of the projects I’m currently working on.

Excel sheet labeled "codes after team meeting on 4/12/19, perceptions on ageing project". Columns are labeled "codes", "descriptions", "decision rules". The rows are labeled "housing", "health" and "preparedness for ageing"

Coding, comparing, counting

Once you have (or are developing) your codes, your next step will be to actually code your data. In most cases, you are looking for your coding structure (your list of codes) to have good coverage . This means that most of the content in your sample should have a code applied to it. If there are large segments of your data that are uncoded, you are potentially missing things. Now, do note that I said most of the time. There are instances when we are using artifacts that may contain a lot of information, only some of which will apply to what we are studying. In these instances, we obviously wouldn’t be expecting the same level of coverage with our codes. As you go about coding you may change, refine and adapt your codebook as you go through your data and compare the information that reflects each code. As you do this, keep your research journal handy and make sure to capture and record these changes so that you have a trail documenting the evolution of your analysis. Also, as suggested earlier, content analysis may also involve some degree of counting as well. You may be keeping a tally of how many times a particular code is represented in your data, thereby offering your reader both a quantification of how many times (and across how many sources) a code was reflected and a narrative description of what that code came to mean.

Representing the findings from your coding scheme

Finally, you need to consider how you will represent the findings from your coding work. This may involve listing out narrative descriptions of codes, visual representations of what each code came to mean or how they related to each other, or a table that includes examples of how your data reflected different elements of your coding structure. However you choose to represent the findings of your content analysis, make sure the resulting product answers your research question and is readily understandable and easy-to-interpret for your audience.

Key Takeaways

Much like thematic analysis, content analysis is concerned with breaking up qualitative data so that you can compare and contrast ideas as you look across all your data, collectively. A couple of distinctions between thematic and content analysis include content analysis’s emphasis on more clearly specifying the unit of analysis used for the purpose of analysis and the flexibility that content analysis offers in comparing across different types of data.
Coding involves both grouping data (after it has been deconstructed) and defining these codes (giving them meaning). If we are using a deductive approach to analysis, we will start with the code defined. If we are using an inductive approach, the code will not be defined until the end of the analysis.

Identify a qualitative research article that uses content analysis (do a quick search of “qualitative” and “content analysis” in your research search engine of choice).

How do the authors display their findings?
What was effective in their presentation?
What was ineffective in their presentation?

Resources for learning more about Content Analysis

Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis .

Colorado State University (n.d.) Writing@CSU Guide: Content analysis .

Columbia University Mailman School of Public Health, Population Health. (n.d.) Methods: Content analysis

Mayring, P. (2000, June). Qualitative content analysis .

A few exemplars of studies employing Content Analysis

Collins et al. (2018). Content analysis of advantages and disadvantages of drinking among individuals with the lived experience of homelessness and alcohol use disorders .

Corley, N. A., & Young, S. M. (2018). Is social work still racist? A content analysis of recent literature .

Deepak et al. (2016). Intersections between technology, engaged learning, and social capital in social work education .

Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15 (3), 398-405. ↵
Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15 (9), 1277-1288. ↵

An approach to data analysis that seeks to identify patterns, trends, or ideas across qualitative data through processes of coding and categorization.

entity that a researcher wants to say something about at the end of her study (individual, group, or organization)

An approach to data analysis in which the researchers begins their analysis using a theory to see if their data fits within this theoretical framework (tests the theory).

An approach to data analysis in which we gather our data first and then generate a theory about its meaning through our analysis.

Part of the qualitative data analysis process where we begin to interpret and assign meaning to the data.

A document that we use to keep track of and define the codes that we have identified (or are using) in our qualitative data analysis.

A decision-rule provides information on how the researcher determines what code should be placed on an item, especially when codes may be similar in nature.

In qualitative data, coverage refers to the amount of data that can be categorized or sorted using the code structure that we are using (or have developed) in our study. With qualitative research, our aim is to have good coverage with our code structure.

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Content analysis

Content analysis is a research method in the social sciences used to reduce large amounts of unstructured textual content into manageable data relevant to the (evaluation) research questions.

Texts refer to any occurrence of communications - including websites, social media, books, essays, interviews, focus groups, diaries, discussions, articles, speeches, conversations, advertising, theatre, informal conversation, and so on. To conduct a content analysis on any such text, the text is broken down into manageable categories on a variety of levels (ie, keywords, word sense, phrase, sentence, or theme) and coded. The coded content can then be quantitatively analyzed for trends, patterns, relationships, similarities, differences etc., from which researchers can get insights and make inferences about the messages within the texts, the writer(s) and the context.

Content analysis uses thematic coding in order to perform a quantitative analysis of particular occurrences of themes in an unstructured text. The coding schedule consists of a table in which each row is a unit for which data is being collected. Each column is a dimension or theme to be analyzed, according to the evaluation questions. Elements of the content are described and organized using these categories. This process is called 'coding' and can enable more efficient sorting and retrieval of data, particularly with the aid of appropriate software. Interpretation of the data may be based on:

frequency of occurrences (e.g. in different samples, or at different times)
patterns of co-occurrence (e.g. ‘Boolean operators’, cluster analysis)
sequence of occurrences.
General advice for using content analysis (archive link)
Two examples in which the content analysis option was used

Busch C, De Maret P S, Flynn T, Kellum R, Le, Brad Meyers S, Saunders M, White R, and Palmquist M. (2005). Content Analysis . Writing@CSU. Colorado State University Department of English. Retrieved from https://writing.colostate.edu/guides/guide.cfm?guideid=61

Power Point Presentation (2007) Introduction to qualitative analysis , Lecture from Psychology course. Retrieved from www.psychology.soton.ac.uk/researchmethods/lectures/media/2007-10-29/qual_lecture3.ppt

List D (2012) Know Your Audience Chapter 16 ? Audience Dialogue Website. Retrieved from http://www.audiencedialogue.net/kya16a.html (archive link)

Expand to view all resources related to 'Content analysis'

Using Word & Excel to analyze qualitative data with Seth Tucker

'Content analysis' is referenced in:

Framework/guide.

Rainbow Framework : Analyse data
Thematic coding

Narrative Analysis 101

Everything you need to know to get started

By: Ethar Al-Saraf (PhD)| Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to research, the host of qualitative analysis methods available to you can be a little overwhelming. In this post, we’ll unpack the sometimes slippery topic of narrative analysis . We’ll explain what it is, consider its strengths and weaknesses , and look at when and when not to use this analysis method.

Overview: Narrative Analysis

What is narrative analysis (simple definition)
The two overarching approaches
The strengths & weaknesses of narrative analysis
When (and when not) to use it
Key takeaways

What Is Narrative Analysis?

Simply put, narrative analysis is a qualitative analysis method focused on interpreting human experiences and motivations by looking closely at the stories (the narratives) people tell in a particular context.

In other words, a narrative analysis interprets long-form participant responses or written stories as data, to uncover themes and meanings . That data could be taken from interviews, monologues, written stories, or even recordings. In other words, narrative analysis can be used on both primary and secondary data to provide evidence from the experiences described.

That’s all quite conceptual, so let’s look at an example of how narrative analysis could be used.

Let’s say you’re interested in researching the beliefs of a particular author on popular culture. In that case, you might identify the characters , plotlines , symbols and motifs used in their stories. You could then use narrative analysis to analyse these in combination and against the backdrop of the relevant context.

This would allow you to interpret the underlying meanings and implications in their writing, and what they reveal about the beliefs of the author. In other words, you’d look to understand the views of the author by analysing the narratives that run through their work.

The Two Overarching Approaches

Generally speaking, there are two approaches that one can take to narrative analysis. Specifically, an inductive approach or a deductive approach. Each one will have a meaningful impact on how you interpret your data and the conclusions you can draw, so it’s important that you understand the difference.

First up is the inductive approach to narrative analysis.

The inductive approach takes a bottom-up view , allowing the data to speak for itself, without the influence of any preconceived notions . With this approach, you begin by looking at the data and deriving patterns and themes that can be used to explain the story, as opposed to viewing the data through the lens of pre-existing hypotheses, theories or frameworks. In other words, the analysis is led by the data.

For example, with an inductive approach, you might notice patterns or themes in the way an author presents their characters or develops their plot. You’d then observe these patterns, develop an interpretation of what they might reveal in the context of the story, and draw conclusions relative to the aims of your research.

Contrasted to this is the deductive approach.

With the deductive approach to narrative analysis, you begin by using existing theories that a narrative can be tested against . Here, the analysis adopts particular theoretical assumptions and/or provides hypotheses, and then looks for evidence in a story that will either verify or disprove them.

For example, your analysis might begin with a theory that wealthy authors only tell stories to get the sympathy of their readers. A deductive analysis might then look at the narratives of wealthy authors for evidence that will substantiate (or refute) the theory and then draw conclusions about its accuracy, and suggest explanations for why that might or might not be the case.

Which approach you should take depends on your research aims, objectives and research questions . If these are more exploratory in nature, you’ll likely take an inductive approach. Conversely, if they are more confirmatory in nature, you’ll likely opt for the deductive approach.

Need a helping hand?

Strengths & Weaknesses

Now that we have a clearer view of what narrative analysis is and the two approaches to it, it’s important to understand its strengths and weaknesses , so that you can make the right choices in your research project.

A primary strength of narrative analysis is the rich insight it can generate by uncovering the underlying meanings and interpretations of human experience. The focus on an individual narrative highlights the nuances and complexities of their experience, revealing details that might be missed or considered insignificant by other methods.

Another strength of narrative analysis is the range of topics it can be used for. The focus on human experience means that a narrative analysis can democratise your data analysis, by revealing the value of individuals’ own interpretation of their experience in contrast to broader social, cultural, and political factors.

All that said, just like all analysis methods, narrative analysis has its weaknesses. It’s important to understand these so that you can choose the most appropriate method for your particular research project.

The first drawback of narrative analysis is the problem of subjectivity and interpretation . In other words, a drawback of the focus on stories and their details is that they’re open to being understood differently depending on who’s reading them. This means that a strong understanding of the author’s cultural context is crucial to developing your interpretation of the data. At the same time, it’s important that you remain open-minded in how you interpret your chosen narrative and avoid making any assumptions .

A second weakness of narrative analysis is the issue of reliability and generalisation . Since narrative analysis depends almost entirely on a subjective narrative and your interpretation, the findings and conclusions can’t usually be generalised or empirically verified. Although some conclusions can be drawn about the cultural context, they’re still based on what will almost always be anecdotal data and not suitable for the basis of a theory, for example.

Last but not least, the focus on long-form data expressed as stories means that narrative analysis can be very time-consuming . In addition to the source data itself, you will have to be well informed on the author’s cultural context as well as other interpretations of the narrative, where possible, to ensure you have a holistic view. So, if you’re going to undertake narrative analysis, make sure that you allocate a generous amount of time to work through the data.

When To Use Narrative Analysis

As a qualitative method focused on analysing and interpreting narratives describing human experiences, narrative analysis is usually most appropriate for research topics focused on social, personal, cultural , or even ideological events or phenomena and how they’re understood at an individual level.

For example, if you were interested in understanding the experiences and beliefs of individuals suffering social marginalisation, you could use narrative analysis to look at the narratives and stories told by people in marginalised groups to identify patterns , symbols , or motifs that shed light on how they rationalise their experiences.

In this example, narrative analysis presents a good natural fit as it’s focused on analysing people’s stories to understand their views and beliefs at an individual level. Conversely, if your research was geared towards understanding broader themes and patterns regarding an event or phenomena, analysis methods such as content analysis or thematic analysis may be better suited, depending on your research aim .

Let’s recap

In this post, we’ve explored the basics of narrative analysis in qualitative research. The key takeaways are:

Narrative analysis is a qualitative analysis method focused on interpreting human experience in the form of stories or narratives .
There are two overarching approaches to narrative analysis: the inductive (exploratory) approach and the deductive (confirmatory) approach.
Like all analysis methods, narrative analysis has a particular set of strengths and weaknesses .
Narrative analysis is generally most appropriate for research focused on interpreting individual, human experiences as expressed in detailed , long-form accounts.

If you’d like to learn more about narrative analysis and qualitative analysis methods in general, be sure to check out the rest of the Grad Coach blog here . Alternatively, if you’re looking for hands-on help with your project, take a look at our 1-on-1 private coaching service .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Research aims, research objectives and research questions

Thanks. I need examples of narrative analysis

Here are some examples of research topics that could utilise narrative analysis:

Personal Narratives of Trauma: Analysing personal stories of individuals who have experienced trauma to understand the impact, coping mechanisms, and healing processes.

Identity Formation in Immigrant Communities: Examining the narratives of immigrants to explore how they construct and negotiate their identities in a new cultural context.

Media Representations of Gender: Analysing narratives in media texts (such as films, television shows, or advertisements) to investigate the portrayal of gender roles, stereotypes, and power dynamics.

Where can I find an example of a narrative analysis table ?

Please i need help with my project,

how can I cite this article in APA 7th style?

please mention the sources as well.

My research is mixed approach. I use interview,key_inforamt interview,FGD and document.so,which qualitative analysis is appropriate to analyze these data.Thanks

Which qualitative analysis methode is appropriate to analyze data obtain from intetview,key informant intetview,Focus group discussion and document.

I’ve finished my PhD. Now I need a “platform” that will help me objectively ascertain the tacit assumptions that are buried within a narrative. Can you help?

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Home » Narrative Analysis – Types, Methods and Examples

Narrative Analysis – Types, Methods and Examples

Table of Contents

Narrative Analysis

Definition:

Narrative analysis is a qualitative research methodology that involves examining and interpreting the stories or narratives people tell in order to gain insights into the meanings, experiences, and perspectives that underlie them. Narrative analysis can be applied to various forms of communication, including written texts, oral interviews, and visual media.

In narrative analysis, researchers typically examine the structure, content, and context of the narratives they are studying, paying close attention to the language, themes, and symbols used by the storytellers. They may also look for patterns or recurring motifs within the narratives, and consider the cultural and social contexts in which they are situated.

Types of Narrative Analysis

Types of Narrative Analysis are as follows:

Content Analysis

This type of narrative analysis involves examining the content of a narrative in order to identify themes, motifs, and other patterns. Researchers may use coding schemes to identify specific themes or categories within the text, and then analyze how they are related to each other and to the overall narrative. Content analysis can be used to study various forms of communication, including written texts, oral interviews, and visual media.

Structural Analysis

This type of narrative analysis focuses on the formal structure of a narrative, including its plot, character development, and use of literary devices. Researchers may analyze the narrative arc, the relationship between the protagonist and antagonist, or the use of symbolism and metaphor. Structural analysis can be useful for understanding how a narrative is constructed and how it affects the reader or audience.

Discourse Analysis

This type of narrative analysis focuses on the language and discourse used in a narrative, including the social and cultural context in which it is situated. Researchers may analyze the use of specific words or phrases, the tone and style of the narrative, or the ways in which social and cultural norms are reflected in the narrative. Discourse analysis can be useful for understanding how narratives are influenced by larger social and cultural structures.

Phenomenological Analysis

This type of narrative analysis focuses on the subjective experience of the narrator, and how they interpret and make sense of their experiences. Researchers may analyze the language used to describe experiences, the emotions expressed in the narrative, or the ways in which the narrator constructs meaning from their experiences. Phenomenological analysis can be useful for understanding how people make sense of their own lives and experiences.

Critical Analysis

This type of narrative analysis involves examining the political, social, and ideological implications of a narrative, and questioning its underlying assumptions and values. Researchers may analyze the ways in which a narrative reflects or reinforces dominant power structures, or how it challenges or subverts those structures. Critical analysis can be useful for understanding the role that narratives play in shaping social and cultural norms.

Autoethnography

This type of narrative analysis involves using personal narratives to explore cultural experiences and identity formation. Researchers may use their own personal narratives to explore issues such as race, gender, or sexuality, and to understand how larger social and cultural structures shape individual experiences. Autoethnography can be useful for understanding how individuals negotiate and navigate complex cultural identities.

Thematic Analysis

This method involves identifying themes or patterns that emerge from the data, and then interpreting these themes in relation to the research question. Researchers may use a deductive approach, where they start with a pre-existing theoretical framework, or an inductive approach, where themes are generated from the data itself.

Narrative Analysis Conducting Guide

Here are some steps for conducting narrative analysis:

Identify the research question: Narrative analysis begins with identifying the research question or topic of interest. Researchers may want to explore a particular social or cultural phenomenon, or gain a deeper understanding of a particular individual’s experience.
Collect the narratives: Researchers then collect the narratives or stories that they will analyze. This can involve collecting written texts, conducting interviews, or analyzing visual media.
Transcribe and code the narratives: Once the narratives have been collected, they are transcribed into a written format, and then coded in order to identify themes, motifs, or other patterns. Researchers may use a coding scheme that has been developed specifically for the study, or they may use an existing coding scheme.
Analyze the narratives: Researchers then analyze the narratives, focusing on the themes, motifs, and other patterns that have emerged from the coding process. They may also analyze the formal structure of the narratives, the language used, and the social and cultural context in which they are situated.
Interpret the findings: Finally, researchers interpret the findings of the narrative analysis, and draw conclusions about the meanings, experiences, and perspectives that underlie the narratives. They may use the findings to develop theories, make recommendations, or inform further research.

Applications of Narrative Analysis

Narrative analysis is a versatile qualitative research method that has applications across a wide range of fields, including psychology, sociology, anthropology, literature, and history. Here are some examples of how narrative analysis can be used:

Understanding individuals’ experiences: Narrative analysis can be used to gain a deeper understanding of individuals’ experiences, including their thoughts, feelings, and perspectives. For example, psychologists might use narrative analysis to explore the stories that individuals tell about their experiences with mental illness.
Exploring cultural and social phenomena: Narrative analysis can also be used to explore cultural and social phenomena, such as gender, race, and identity. Sociologists might use narrative analysis to examine how individuals understand and experience their gender identity.
Analyzing historical events: Narrative analysis can be used to analyze historical events, including those that have been recorded in literary texts or personal accounts. Historians might use narrative analysis to explore the stories of survivors of historical traumas, such as war or genocide.
Examining media representations: Narrative analysis can be used to examine media representations of social and cultural phenomena, such as news stories, films, or television shows. Communication scholars might use narrative analysis to examine how news media represent different social groups.
Developing interventions: Narrative analysis can be used to develop interventions to address social and cultural problems. For example, social workers might use narrative analysis to understand the experiences of individuals who have experienced domestic violence, and then use that knowledge to develop more effective interventions.

Examples of Narrative Analysis

Here are some examples of how narrative analysis has been used in research:

Personal narratives of illness: Researchers have used narrative analysis to examine the personal narratives of individuals living with chronic illness, to understand how they make sense of their experiences and construct their identities.
Oral histories: Historians have used narrative analysis to analyze oral histories to gain insights into individuals’ experiences of historical events and social movements.
Children’s stories: Researchers have used narrative analysis to analyze children’s stories to understand how they understand and make sense of the world around them.
Personal diaries : Researchers have used narrative analysis to examine personal diaries to gain insights into individuals’ experiences of significant life events, such as the loss of a loved one or the transition to adulthood.
Memoirs : Researchers have used narrative analysis to analyze memoirs to understand how individuals construct their life stories and make sense of their experiences.
Life histories : Researchers have used narrative analysis to examine life histories to gain insights into individuals’ experiences of migration, displacement, or social exclusion.

Purpose of Narrative Analysis

The purpose of narrative analysis is to gain a deeper understanding of the stories that individuals tell about their experiences, identities, and beliefs. By analyzing the structure, content, and context of these stories, researchers can uncover patterns and themes that shed light on the ways in which individuals make sense of their lives and the world around them.

The primary purpose of narrative analysis is to explore the meanings that individuals attach to their experiences. This involves examining the different elements of a story, such as the plot, characters, setting, and themes, to identify the underlying values, beliefs, and attitudes that shape the story. By analyzing these elements, researchers can gain insights into the ways in which individuals construct their identities, understand their relationships with others, and make sense of the world.

Narrative analysis can also be used to identify patterns and themes across multiple stories. This involves comparing and contrasting the stories of different individuals or groups to identify commonalities and differences. By analyzing these patterns and themes, researchers can gain insights into broader cultural and social phenomena, such as gender, race, and identity.

In addition, narrative analysis can be used to develop interventions that address social and cultural problems. By understanding the stories that individuals tell about their experiences, researchers can develop interventions that are tailored to the unique needs of different individuals and groups.

Overall, the purpose of narrative analysis is to provide a rich, nuanced understanding of the ways in which individuals construct meaning and make sense of their lives. By analyzing the stories that individuals tell, researchers can gain insights into the complex and multifaceted nature of human experience.

When to use Narrative Analysis

Here are some situations where narrative analysis may be appropriate:

Studying life stories: Narrative analysis can be useful in understanding how individuals construct their life stories, including the events, characters, and themes that are important to them.
Analyzing cultural narratives: Narrative analysis can be used to analyze cultural narratives, such as myths, legends, and folktales, to understand their meanings and functions.
Exploring organizational narratives: Narrative analysis can be helpful in examining the stories that organizations tell about themselves, their histories, and their values, to understand how they shape the culture and practices of the organization.
Investigating media narratives: Narrative analysis can be used to analyze media narratives, such as news stories, films, and TV shows, to understand how they construct meaning and influence public perceptions.
Examining policy narratives: Narrative analysis can be helpful in examining policy narratives, such as political speeches and policy documents, to understand how they construct ideas and justify policy decisions.

Characteristics of Narrative Analysis

Here are some key characteristics of narrative analysis:

Focus on stories and narratives: Narrative analysis is concerned with analyzing the stories and narratives that people tell, whether they are oral or written, to understand how they shape and reflect individuals’ experiences and identities.
Emphasis on context: Narrative analysis seeks to understand the context in which the narratives are produced and the social and cultural factors that shape them.
Interpretive approach: Narrative analysis is an interpretive approach that seeks to identify patterns and themes in the stories and narratives and to understand the meaning that individuals and communities attach to them.
Iterative process: Narrative analysis involves an iterative process of analysis, in which the researcher continually refines their understanding of the narratives as they examine more data.
Attention to language and form : Narrative analysis pays close attention to the language and form of the narratives, including the use of metaphor, imagery, and narrative structure, to understand the meaning that individuals and communities attach to them.
Reflexivity : Narrative analysis requires the researcher to reflect on their own assumptions and biases and to consider how their own positionality may shape their interpretation of the narratives.
Qualitative approach: Narrative analysis is typically a qualitative research method that involves in-depth analysis of a small number of cases rather than large-scale quantitative studies.

Advantages of Narrative Analysis

Here are some advantages of narrative analysis:

Rich and detailed data : Narrative analysis provides rich and detailed data that allows for a deep understanding of individuals’ experiences, emotions, and identities.
Humanizing approach: Narrative analysis allows individuals to tell their own stories and express their own perspectives, which can help to humanize research and give voice to marginalized communities.
Holistic understanding: Narrative analysis allows researchers to understand individuals’ experiences in their entirety, including the social, cultural, and historical contexts in which they occur.
Flexibility : Narrative analysis is a flexible research method that can be applied to a wide range of contexts and research questions.
Interpretive insights: Narrative analysis provides interpretive insights into the meanings that individuals attach to their experiences and the ways in which they construct their identities.
Appropriate for sensitive topics: Narrative analysis can be particularly useful in researching sensitive topics, such as trauma or mental health, as it allows individuals to express their experiences in their own words and on their own terms.
Can lead to policy implications: Narrative analysis can provide insights that can inform policy decisions and interventions, particularly in areas such as health, education, and social policy.

Limitations of Narrative Analysis

Here are some of the limitations of narrative analysis:

Subjectivity : Narrative analysis relies on the interpretation of researchers, which can be influenced by their own biases and assumptions.
Limited generalizability: Narrative analysis typically involves in-depth analysis of a small number of cases, which limits its generalizability to broader populations.
Ethical considerations: The process of eliciting and analyzing narratives can raise ethical concerns, particularly when sensitive topics such as trauma or abuse are involved.
Limited control over data collection: Narrative analysis often relies on data that is already available, such as interviews, oral histories, or written texts, which can limit the control that researchers have over the quality and completeness of the data.
Time-consuming: Narrative analysis can be a time-consuming research method, particularly when analyzing large amounts of data.
Interpretation challenges: Narrative analysis requires researchers to make complex interpretations of data, which can be challenging and time-consuming.
Limited statistical analysis: Narrative analysis is typically a qualitative research method that does not lend itself well to statistical analysis.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Textual Analysis: Definition, Types & 10 Examples

textual analysis example and definition, explained below

Textual analysis is a research methodology that involves exploring written text as empirical data. Scholars explore both the content and structure of texts, and attempt to discern key themes and statistics emergent from them.

This method of research is used in various academic disciplines, including cultural studies, literature, bilical studies, anthropology , sociology, and others (Dearing, 2022; McKee, 2003).

This method of analysis involves breaking down a text into its constituent parts for close reading and making inferences about its context, underlying themes, and the intentions of its author.

Textual Analysis Definition

Alan McKee is one of the preeminent scholars of textual analysis. He provides a clear and approachable definition in his book Textual Analysis: A Beginner’s Guide (2003) where he writes:

“When we perform textual analysis on a text we make an educated guess at some of the most likely interpretations that might be made of the text […] in order to try and obtain a sense of the ways in which, in particular cultures at particular times, people make sense of the world around them.”

A key insight worth extracting from this definition is that textual analysis can reveal what cultural groups value, how they create meaning, and how they interpret reality.

This is invaluable in situations where scholars are seeking to more deeply understand cultural groups and civilizations – both past and present (Metoyer et al., 2018).

As such, it may be beneficial for a range of different types of studies, such as:

Studies of Historical Texts: A study of how certain concepts are framed, described, and approached in historical texts, such as the Bible.
Studies of Industry Reports: A study of how industry reports frame and discuss concepts such as environmental and social responsibility.
Studies of Literature: A study of how a particular text or group of texts within a genre define and frame concepts. For example, you could explore how great American literature mythologizes the concept of the ‘The American Dream’.
Studies of Speeches: A study of how certain politicians position national identities in their appeals for votes.
Studies of Newspapers: A study of the biases within newspapers toward or against certain groups of people.
Etc. (For more, see: Dearing, 2022)

McKee uses the term ‘textual analysis’ to also refer to text types that are not just written, but multimodal. For a dive into the analysis of multimodal texts, I recommend my article on content analysis , where I explore the study of texts like television advertisements and movies in detail.

Features of a Textual Analysis

When conducting a textual analysis, you’ll need to consider a range of factors within the text that are worthy of close examination to infer meaning. Features worthy of considering include:

Content: What is being said or conveyed in the text, including explicit and implicit meanings, themes, or ideas.
Context: When and where the text was created, the culture and society it reflects, and the circumstances surrounding its creation and distribution.
Audience: Who the text is intended for, how it’s received, and the effect it has on its audience.
Authorship: Who created the text, their background and perspectives, and how these might influence the text.
Form and structure: The layout, sequence, and organization of the text and how these elements contribute to its meanings (Metoyer et al., 2018).

Textual Analysis Coding Methods

The above features may be examined through quantitative or qualitative research designs , or a mixed-methods angle.

1. Quantitative Approaches

You could analyze several of the above features, namely, content, form, and structure, from a quantitative perspective using computational linguistics and natural language processing (NLP) analysis.

From this approach, you would use algorithms to extract useful information or insights about frequency of word and phrase usage, etc. This can include techniques like sentiment analysis, topic modeling, named entity recognition, and more.

2. Qualitative Approaches

In many ways, textual analysis lends itself best to qualitative analysis. When identifying words and phrases, you’re also going to want to look at the surrounding context and possibly cultural interpretations of what is going on (Mayring, 2015).

Generally, humans are far more perceptive at teasing out these contextual factors than machines (although, AI is giving us a run for our money).

One qualitative approach to textual analysis that I regularly use is inductive coding, a step-by-step methodology that can help you extract themes from texts. If you’re interested in using this step-by-step method, read my guide on inductive coding here .

See more Qualitative Research Approaches Here

Textual Analysis Examples

Title: “Discourses on Gender, Patriarchy and Resolution 1325: A Textual Analysis of UN Documents” Author: Nadine Puechguirbal Year: 2010 APA Citation: Puechguirbal, N. (2010). Discourses on Gender, Patriarchy and Resolution 1325: A Textual Analysis of UN Documents, International Peacekeeping, 17 (2): 172-187. doi: 10.1080/13533311003625068

Summary: The article discusses the language used in UN documents related to peace operations and analyzes how it perpetuates stereotypical portrayals of women as vulnerable individuals. The author argues that this language removes women’s agency and keeps them in a subordinate position as victims, instead of recognizing them as active participants and agents of change in post-conflict environments. Despite the adoption of UN Security Council Resolution 1325, which aims to address the role of women in peace and security, the author suggests that the UN’s male-dominated power structure remains unchallenged, and gender mainstreaming is often presented as a non-political activity.

Title: “Racism and the Media: A Textual Analysis” Author: Kassia E. Kulaszewicz Year: 2015 APA Citation: Kulaszewicz, K. E. (2015). Racism and the Media: A Textual Analysis . Dissertation. Retrieved from: https://sophia.stkate.edu/msw_papers/477

Summary: This study delves into the significant role media plays in fostering explicit racial bias. Using Bandura’s Learning Theory, it investigates how media content influences our beliefs through ‘observational learning’. Conducting a textual analysis, it finds differences in representation of black and white people, stereotyping of black people, and ostensibly micro-aggressions toward black people. The research highlights how media often criminalizes Black men, portraying them as violent, while justifying or supporting the actions of White officers, regardless of their potential criminality. The study concludes that news media likely continues to reinforce racism, whether consciously or unconsciously.

Title: “On the metaphorical nature of intellectual capital: a textual analysis” Author: Daniel Andriessen Year: 2006 APA Citation: Andriessen, D. (2006). On the metaphorical nature of intellectual capital: a textual analysis. Journal of Intellectual capital , 7 (1), 93-110.

Summary: This article delves into the metaphorical underpinnings of intellectual capital (IC) and knowledge management, examining how knowledge is conceptualized through metaphors. The researchers employed a textual analysis methodology, scrutinizing key texts in the field to identify prevalent metaphors. They found that over 95% of statements about knowledge are metaphor-based, with “knowledge as a resource” and “knowledge as capital” being the most dominant. This study demonstrates how textual analysis helps us to understand current understandings and ways of speaking about a topic.

Title: “Race in Rhetoric: A Textual Analysis of Barack Obama’s Campaign Discourse Regarding His Race” Author: Andrea Dawn Andrews Year: 2011 APA Citation: Andrew, A. D. (2011) Race in Rhetoric: A Textual Analysis of Barack Obama’s Campaign Discourse Regarding His Race. Undergraduate Honors Thesis Collection. 120 . https://digitalcommons.butler.edu/ugtheses/120

This undergraduate honors thesis is a textual analysis of Barack Obama’s speeches that explores how Obama frames the concept of race. The student’s capstone project found that Obama tended to frame racial inequality as something that could be overcome, and that this was a positive and uplifting project. Here, the student breaks-down times when Obama utilizes the concept of race in his speeches, and examines the surrounding content to see the connotations associated with race and race-relations embedded in the text. Here, we see a decidedly qualitative approach to textual analysis which can deliver contextualized and in-depth insights.

Sub-Types of Textual Analysis

While above I have focused on a generalized textual analysis approach, a range of sub-types and offshoots have emerged that focus on specific concepts, often within their own specific theoretical paradigms. Each are outlined below, and where I’ve got a guide, I’ve linked to it in blue:

Content Analysis : Content analysis is similar to textual analysis, and I would consider it a type of textual analysis, where it’s got a broader understanding of the term ‘text’. In this type, a text is any type of ‘content’, and could be multimodal in nature, such as television advertisements, movies, posters, and so forth. Content analysis can be both qualitative and quantitative, depending on whether it focuses more on the meaning of the content or the frequency of certain words or concepts (Chung & Pennebaker, 2018).
Discourse Analysis : Emergent specifically from critical and postmodern/ poststructural theories, discourse analysis focuses closely on the use of language within a social context, with the goal of revealing how repeated framing of terms and concepts has the effect of shaping how cultures understand social categories. It considers how texts interact with and shape social norms, power dynamics, ideologies, etc. For example, it might examine how gender is socially constructed as a distinct social category through Disney films. It may also be called ‘critical discourse analysis’.
Narrative Analysis: This approach is used for analyzing stories and narratives within text. It looks at elements like plot, characters, themes, and the sequence of events to understand how narratives construct meaning.
Frame Analysis: This approach looks at how events, ideas, and themes are presented or “framed” within a text. It explores how these frames can shape our understanding of the information being presented. While similar to discourse analysis, a frame analysis tends to be less associated with the loaded concept of ‘discourse’ that exists specifically within postmodern paradigms (Smith, 2017).
Semiotic Analysis: This approach studies signs and symbols, both visual and textual, and could be a good compliment to a content analysis, as it provides the language and understandings necessary to describe how signs make meaning in cultural contexts that we might find with the fields of semantics and pragmatics . It’s based on the theory of semiotics, which is concerned with how meaning is created and communicated through signs and symbols.
Computational Textual Analysis: In the context of data science or artificial intelligence, this type of analysis involves using algorithms to process large amounts of text. Techniques can include topic modeling, sentiment analysis, word frequency analysis, and others. While being extremely useful for a quantitative analysis of a large dataset of text, it falls short in its ability to provide deep contextualized understandings of words-in-context.

Each of these methods has its strengths and weaknesses, and the choice of method depends on the research question, the type of text being analyzed, and the broader context of the research.

See More Examples of Analysis Here

Strengths and Weaknesses of Textual Analysis

When writing your methodology for your textual analysis, make sure to define not only what textual analysis is, but (if applicable) the type of textual analysis, the features of the text you’re analyzing, and the ways you will code the data. It’s also worth actively reflecting on the potential weaknesses of a textual analysis approach, but also explaining why, despite those weaknesses, you believe this to be the most appropriate methodology for your study.

Chung, C. K., & Pennebaker, J. W. (2018). Textual analysis. In Measurement in social psychology (pp. 153-173). Routledge.

Dearing, V. A. (2022). Manual of textual analysis . Univ of California Press.

McKee, A. (2003). Textual analysis: A beginner’s guide. Textual analysis , 1-160.

Mayring, P. (2015). Qualitative content analysis: Theoretical background and procedures. Approaches to qualitative research in mathematics education: Examples of methodology and methods , 365-380. doi: https://doi.org/10.1007/978-94-017-9181-6_13

Metoyer, R., Zhi, Q., Janczuk, B., & Scheirer, W. (2018, March). Coupling story to visualization: Using textual analysis as a bridge between data and interpretation. In 23rd International Conference on Intelligent User Interfaces (pp. 503-507). doi: https://doi.org/10.1145/3172944.3173007

Smith, J. A. (2017). Textual analysis. The international encyclopedia of communication research methods , 1-7.

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 100 Consumer Goods Examples
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Topic Analysis: The Ultimate Guide

How does it work?

Use Cases & Applications

Topic analysis is a Natural Language Processing (NLP) technique that allows us to automatically extract meaning from text by identifying recurrent themes or topics.

Automatically detect topics in text data

Businesses deal with large volumes of unstructured text every day like emails, support tickets, social media posts, online reviews, etc.

When it comes to analyzing huge amounts of text data, it’s just too big a task to do manually. It’s also tedious, time-consuming, and expensive. Manually sorting through large amounts of data is more likely to lead to mistakes and inconsistencies. Plus, it doesn’t scale well.

The good news is that AI-guided topic analysis makes it easier, faster, and more accurate to analyze unstructured data.

Read this guide to learn more about topic analysis, its applications, and how to get started with no-code tools like MonkeyLearn .

Introduction to Topic Analysis

What is topic analysis, scope of topic analysis, when is topic analysis used, why is topic analysis important, topic analysis examples, how does topic analysis work.

Topic Modeling Vs Topic Classification

Topic Modeling

Topic classification.

Topic Analysis Use Cases and Applications

Topic Analysis APIs

Courses and Lectures

Let's get started!

Topic analysis (also called topic detection, topic modeling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning “tags” or categories according to each individual text’s topic or theme.

Topic analysis uses natural language processing (NLP) to break down human language so that you can find patterns and unlock semantic structures within texts to extract insights and help make data-driven decisions.

The two most common approaches for topic analysis with machine learning are NLP topic modeling and NLP topic classification .

Topic modeling is an unsupervised machine learning technique. This means it can infer patterns and cluster similar expressions without needing to define topic tags or train data beforehand. This type of algorithm can be applied quickly and easily, but there’s a downside – they are rather inaccurate .

Text classification or topic extraction from text, on the other hand, needs to know the topics of a text before starting the analysis, because you need to tag data in order to train a topic classifier. Although there’s an extra step involved, topic classifiers pay off in the long run, and they’re much more precise than clustering techniques.

We’ll look more closely at these two approaches in the section How It Works .

Topic analysis can be applied at different levels of scope:

Document-level : the topic model obtains the different topics from within a complete text. For example, the topics of an email or a news article.
Sentence-level : the topic model obtains the topic of a single sentence. For example, the topic of a news article headline.
Sub-sentence level : the topic model obtains the topic of sub-expressions from within a sentence. For example, different topics within a single sentence of a product review.

Topic tagging is particularly useful to analyze huge amounts of text data in a fast and cost-effective way – from internal documents, communications with customers, or all over the web. Yes, you could do it manually but, let’s face it, when there’s too much information to be classified, it will just end up being time-consuming, expensive, and much less accurate.

At MonkeyLearn , we help companies use topic analysis to make their teams more efficient, automate business processes, get valuable insights from data, and save hours of manual data processing.

Imagine you need to analyze a large dataset of reviews to find out what people are saying about your product . You could combine topic labeling with sentiment analysis to discover which aspects or features (topics) of your product are being discussed most often, and determine how people feel about them (are their statements positive, negative or neutral?). This technique is known as aspect-based sentiment analysis .

Besides brand monitoring, there are many other uses for topic analysis, such as social media monitoring, customer service, voice of customer (VoC) analysis, business intelligence, sales and marketing, SEO, product analytics and knowledge management.

Businesses generate and collect massive amounts of data every day. Analyzing and processing this data using automated topic analysis methods will help businesses make better decisions, optimize internal processes, identify trends, and provide all sorts of other advantages to make them more efficient and productive.

When it comes to sorting through all this data, machine learning models are crucial. Topic detection allows us to easily scan large documents and find out what customers are talking about.

Benefits of topic modeling include:

Data analysis at scale

If you had to manually detect topics by sifting through a huge database, not only would it be very time-consuming, it would also be far too expensive. Automated topic analysis with machine learning makes it possible to scan as much data as you want, providing brand new opportunities to obtain meaningful insights.

Real-time analysis

By combining topic tagging with other types of natural language processing techniques, like sentiment analysis, you can obtain a real-time picture of what your clients are saying about your product. And most importantly, you can use that information to make data-driven decisions, 24/7 and in real time.

Consistent Criteria

Automated topic analysis is based on natural language processing (NLP) – a combination of statistics, computational linguistics, and computer science – so you can expect high-quality results with unsurpassed accuracy.

Here are some examples to help you better understand the potential uses of automatic topic analysis:

Topic labeling is used to identify the topic of a news headline. What is a news article talking about? Is it Politics , Sport , or Economy ? For example:

“iPhone sales drop 20 percent in China as Huawei gains market share”

A topic model would infer the general topic of this headline is Economy by identifying words and expressions related to this topic ( sales - drop - percent - China - gains - market share ).

Topic analysis is used to automatically understand which type of issue is being reported on any given Customer Support Ticket. Is this ticket about Billing Issues , Account Issues or Shipping Issues ? For example:

“My order hasn’t arrived yet” will be tagged as a Shipping Issue.

Topic analysis can be used to analyze open-ended questions in customer satisfaction surveys , to find what aspect of the product or service the customer is referring to. For example:

Question: “What is the one thing that we could do to improve your experience with [ product]?” Answer: “Improve the shopping cart experience, it’s super confusing.”

The topic of this answer is UI/UX .

You’ll discover the most frequently discussed topics about your product or service, uncover new trends just as they emerge, and streamline processes by letting machines perform tasks for you.

Topic analysis models are able to detect topics in a text with advanced machine learning algorithms that count words and find and group similar word patterns.

Let's imagine you want to find out what customers are saying about various features of a new laptop.

Your topics of interest might be Portability , Design , and Price . Now, instead of going through rows and rows of customer feedback with a fine-tooth comb, in an attempt to separate feedback into topics of interest, you'll be able to run a topic analysis.

For Price, analysis models might detect patterns such as currency symbols followed by numbers, related words (affordability, expensive, cheap) , synonyms (cost, price tag, worth) or phrases (worth every penny) , and label the corresponding text accordingly.

Now, let's imagine you don't have a list of predetermined topics. Topic analysis models can also detect topics by counting word frequency and the distance between each word use. Sounds simple, right? Well, it's a little more complicated than just counting and spotting patterns.

Topic Modeling vs Topic Classification

There are many approaches and techniques you can use to automatically analyze the topics of a set of texts, and the one you decide to use depends on the problem at hand. To understand the ins and outs of how topic analysis models work, we're going to focus on the two most common approaches.

If you just have a bunch of texts and want to figure out what topics these texts cover, what you're looking for is topic modeling .

Now, if you already know the possible topics for your texts and want to automatically tag them with the relevant topic, you want topic classification.

Enter machine learning . It can be used to automate tedious and time-consuming manual tasks. There are many machine learning algorithms that, given a set of documents and a couple of friendly nudges, are able to automatically infer the topics on the dataset, based on the content of the texts themselves.

The majority of these algorithms are unsupervised , which means that you feed them the texts and training parameters and they do the rest. Topic modeling runs on this kind of algorithm.

On the other hand, you have supervised algorithms. Machines are fed examples of data labeled according to their topics so that they eventually learn how to tag text by topic by themselves. These are the types of algorithms commonly used for topic classification.

Unsupervised machine learning algorithms are, in theory, less work-intensive than supervised algorithms, since they don't require human tagged data. They may, however, require quality data in large quantities.

In this case, it may be advantageous to just run unsupervised algorithms and discover topics in the text, as part of an analysis process.

Having said this, topic modeling algorithms will not deliver neatly packaged topics with labels such as Sports and Politics . Rather, they will churn out collections of documents that the algorithm thinks are related, and specific terms that it used to infer these relations. It will be your job to figure out what these relations actually mean.

On the other hand, supervised machine learning algorithms require that you go through the legwork of explaining to the machine what it is that you want, via the tagged examples that you feed it. Therefore, the topic definition and tagging process are important steps that should not be taken lightly, since they make or break the real-life performance of the model.

The advantages of supervised algorithms win hands down, though. You can refine your criteria and define your topics, and if you're consistent in the labeling of your texts, you will be rewarded with a model that will classify new, unseen samples according to their topics, the same way you would.

Now, let's go further and understand how both topic modeling and topic classification actually work.

Topic modeling is used when you have a set of text documents (such as emails, survey responses, support tickets, product reviews, etc), and you want to find out the different topics that they cover and group them by those topics.

The way these algorithms work is by assuming that each document is composed of a mixture of topics, and then trying to find out how strong a presence each topic has in a given document. This is done by grouping together the documents based on the words they contain, and noticing correlations between them.

Although similar, topic modeling shouldn't be confused with cluster analysis .

To better understand the ideas behind topic modeling, we will cover the basics of two of the most popular algorithms: LSA and LDA.

Latent Semantic Analysis (LSA)

Latent Semantic Analysis is the ‘traditional' method for topic modeling. It is based on a principle called the distributional hypothesis : words and expressions that occur in similar pieces of text will have similar meanings.

Like Naive Bayes , it is based on the word frequencies of the dataset. The general idea is that for every word in each document, you can count the frequency of that word and group together the documents that have high frequencies of the same words.

Now, to dive a little bit more into how this is actually done, let's first clarify what is meant by word frequency. The frequency of a word or term in a document is a number that indicates how often a word appears in a document. That's right, these algorithms ignore syntax and semantics such as word order, meaning, and grammar, and just treat every document as an unsorted “ bag of words .”

The frequency can be calculated simply by counting – if the word cat appears 10 times in a document, then its frequency is 10. This approach proves to be a bit limited, so tf-idf is normally used. Tf-idf takes into account how common a word is overall (in all documents) vs. how common it is in a specific document, so more common words are ranked higher since they are considered a better “representation” of a document, even if they are not the most numerous.

After doing the word frequency calculation, we are left with a matrix that has a row for every word and a column for every document. Each cell is the calculated frequency for that word in that document. This is the document-term matrix ; it relates documents to terms.

Hidden inside it is what we want: a document-topic matrix and a term-topic matrix , which relate documents to topics and terms to topics. These matrices are the ones that show information about the topics of the texts.

The way these matrices are generated is by decomposing the document-term matrix into three matrices using a technique called truncated SVD . Firstly, singular-value decomposition (SVD) is a linear algebra algorithm for factorizing a matrix into the product of three matrices U * S * V .The important part is that the middle matrix S is a diagonal matrix of the singular values of the original matrix. For LSA, every one of the singular values represents a potential topic.

Truncated SVD selects the largest t singular values and keeps the first t columns of U and the first t rows of V , reducing the dimensionality of the original decomposition. t will be the number of topics that the algorithm finds, so it's a hyperparameter that will need tuning. The idea is that the most important topics are selected, and U is the document-topic matrix and V is the term-topic matrix.

The vectors that make up these matrices represent documents expressed with topics, and terms expressed with topics; they can be measured with techniques such as cosine similarity to evaluate.

Latent Dirichlet Allocation (LDA)

Here is where things start getting a bit technical. Understanding LDA fully involves some advanced mathematical probability topics. However, the basic idea behind it is more easily understood.

Imagine a fixed set of topics. We define each topic as represented by an (unknown) set of words. These are the topics that our documents cover, but we don't know what they are yet. LDA tries to map all the (known) documents to the (unknown) topics in a way such that the words in each document are mostly captured by those topics.

The fundamental assumption here is the same used for LSA: documents with the same topic will use similar words. It's also assumed that every document is composed of a mixture of topics, and every word has a probability of belonging to a certain topic.

LDA assumes documents are generated the following way: pick a mixture of topics (say, 20% topic A, 80% topic B, and 0% topic C) and then pick words that belong to those topics. The words are picked at random according to how likely they are to appear in a certain document.

Of course, in real life documents aren't written this way. Documents are written by humans and have characteristics that make them readable, such as word order, grammar, etc. However, it can be argued that just by looking at the words of a document, you can detect the topic, even if the actual message of the document doesn't come through.

This is what LDA does. It sees a document and assumes that it was generated as described above. Then it works backward from the words that make up the document and tries to guess the mixture of topics that resulted in that particular arrangement of words.

The way this is achieved exceeds the scope of this article but if you'd like to learn more, a good starting point is the original LDA paper .

Something we should mention about the implementation is that it has two hyperparameters for training, usually called α (alpha) and β (beta). Knowing what these do is important for using libraries that implement the algorithm.

Alpha controls the similarity of documents. A low value will represent documents as a mixture of few topics, while a high value will output document representations of more topics -- making all the documents appear more similar to each other.

Beta is the same but for topics, so it controls topic similarity. A low value will represent topics as more distinct by making fewer, more unique words belong to each topic. A high value will have the opposite effect, resulting in topics containing more words in common.

Another important thing that has to be specified before training is the number of topics that the model will have. The algorithm cannot decide this by itself, it needs to be told how many topics to find. Then, the output for every document will be the mixture of topics that each particular document has. This output is just a vector, a list of numbers that means "for topic A, 0.2; for topic B, 0.7; ..." and so on. These vectors can be compared in different ways, and these comparisons are useful for understanding the corpus, to get an idea of its fundamental structures.

Unlike topic modeling, in topic classification you already know what your topics are.

For example, you may want to categorize customer support tickets by Software Issue and Billing Issue . What you want to do is assign one of these topics to each of the tickets, usually to speed up and automate some human-dependent processes. For example, you could automatically route support tickets , sorted by topic, to the correct person on the team without having to sift through them manually.

Unlike the algorithms for topic modeling, the machine learning algorithms used for topic classification are supervised. This means you need to feed them documents already labeled by topic, and the algorithms learn how to label new, unseen documents with these topics.

Now, how you predetermine topics for your documents is a different issue entirely. If you're looking to automate some already existing task, then you probably have a good idea about the topics of your texts. In other cases, you could use the previously discussed topic modeling methods as a way to better understand the content of your documents beforehand.

What ends up happening in real-life scenarios is that the topics are uncovered as the model is built.

Since automated classification – either by rules or machine learning – always involves a first step of manually analyzing and tagging texts, you usually end up refining your topic set as you go. Before you can consider the model finished, your topics should be solid and your dataset consistent.

Next, we will cover the main paths for automated topic classification: rule-based systems, machine learning systems, and hybrid systems.

Rule-Based Systems

Before getting into machine learning algorithms, it's important to note that it's possible to build a topic classifier entirely by hand, without machine learning.

The way this works is by directly programming a set of hand-made rules based on the content of the documents that a human expert actually read. The idea is that the rules represent the codified knowledge of the expert, and are able to discern between documents of different topics by looking directly at semantically relevant elements of a text, and at the metadata that a document may have. Each one of these rules consists of a pattern and a prediction (in this case, a predicted topic).

Back to support tickets, a way to solve this problem using rules would be to define lists of words, one for each topic (e.g., for Software Issue words like bug, program, crash , etc., and for Billing Issue words like price, charge, invoice, $ , and so on). Now, when a new ticket comes in, you count the frequency of software-related words and billing-related words. Then, the topic with the highest frequency gets the new ticket assigned to it.

Rule-based systems such as this are human comprehensible ; a person can sit down, read the rules, and understand how a model works. Over time it's possible to improve them by refining existing rules and adding new ones.

However, there are some disadvantages. First, these systems require deep knowledge of the domain (remember that we used the word expert ? It's not a coincidence). They also require a lot of work, because creating rules for a complex system can be quite difficult and requires a lot of analysis and testing to make sure it's working as intended. Lastly, rule-based systems are a pain to maintain and don't scale very well, because adding new rules will affect the performance of the rules that were already in place.

Machine Learning Systems

In machine learning classification, examples of text and the expected categories (AKA training data) are used to train an NLP topic classification model. This model learns from the training data (with the help of natural language processing) to recognize patterns and classify the text into the categories you define.

First, training data has to be transformed into something a machine can understand, that is, vectors (i.e. lists of numbers which encode information). By using vectors, the model can extract relevant pieces of information (features) which will help it learn from the training data and make predictions. There are different methods to achieve this, but one of the most used is known as the bag of words vectorization. Learn more about text vectorization

Once the training data is transformed into vectors, they are fed to an algorithm which uses them to produce a model that is able to classify the texts to come:

For making new predictions, the trained model transforms an incoming text into a vector, extracts its relevant features, and makes a prediction:

The classification model can be improved by training it with more data and changing the training parameters of the algorithm; these are known as hyperparameters.

The following are broad-stroke overviews of machine learning algorithms that can be used for topic classification. For a more in-depth explanation of each, check out the linked articles.

Naive Bayes

Naive Bayes is a family of simple algorithms that usually give great results from small amounts of training data and limited computational resources. The most popular member of the family is probably Multinomial Naive Bayes (MNB), and it's one of the algorithms that MonkeyLearn uses.

Similar to LSA, MNB correlates the probability of words appearing in a text with the probability of that text being about a certain topic. The main difference between the two is what is done with the data afterwards: LSA looks for patterns in the existing dataset, while MNB uses the existing dataset to make predictions for new texts.

Support Vector Machines

Although based on a simple idea, Support Vector Machines (SVM) is more complex than Naive Bayes, so it requires more computational power, but it usually gives better. However, it's possible to get training times similar to those of an MNB classifier with optimization by feature selection, in addition to running an optimized linear kernel such as scikit-learn's LinearSVC .

The basic idea for SVM is, once all the texts are vectorized (so they are points in mathematical space), to find the best line (in higher dimensional space called a hyperplane ) that separates these vectors into the desired topics. Then, when a new text comes in, vectorize it and take a look at which side of the line it ends up: that's the output topic.

Deep Learning

Deep learning is actually a catch-all term for a family of algorithms loosely inspired by the way human neurons work. Although the ideas behind artificial neural networks originate in the 1950s, these algorithms have seen a great resurgence in recent years thanks to the decline of computing costs, the increase of computing power, and the availability of huge amounts of data.

Text classification , in general, and topic classification in particular, have greatly benefited from this resurgence and usually offer great results in exchange for some draconian computational requirements. It's not unusual for deep learning models to train for days, weeks, or even months.

For topic classification, the two main deep learning architectures used are Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The differences are outside the scope of this article, but here's a good comparison with some real-world benchmarks.

Although deep learning algorithms require much more training data than traditional machine learning algorithms, deep learning classifiers continue to get better the more data they have. On the other hand, traditional machine learning algorithms, such as SVM and MNB, reach a limit, after which they can't improve even with more training data:

Deep Learning vs Traditional Machine Learning algorithms

This doesn't mean that the other algorithms are strictly worse; it depends on the task at hand. For instance, spam detection was declared "solved" a couple of decades ago using just Naive Bayes and n-grams.

Other deep learning algorithms like Word2Vec or GloVe are also used; these are great for getting better vector representations for words when training with other, traditional machine learning algorithms.

Hybrid Systems

The idea behind hybrid systems is to combine a base machine learning classifier with a rule-based system that improves the results with fine-tuned rules. These rules can be used to correct topics that haven't been correctly modeled by the base classifier.

Metrics and evaluation

Training models is great and all, but unless you have a regular, consistent way to measure your results, you won't be able to judge the operability or improvability of your model.

In order to measure the performance of a model, you'll need to let it categorize texts that you already know which topic category they fall under, and see how it performed.

The basic metrics analyzed are:

Accuracy : the percentage of texts that were predicted with the correct topic
Precision : the percentage of texts the model got right out of the total number of texts that it predicted for a given topic
Recall : the percentage of texts the model predicted for a given topic out of the total number of texts it should have predicted for that topic
F1 Score : the harmonic mean of precision and recall

One important note: you shouldn’t use training data to measure performance, as the model has already seen these samples.

The ideal way to measure would be to take part of the manually tagged data, don't use it to train, and when the model is trained then use it to test. This dataset is called a golden standard. However, leaving part of your hard-earned data just lying around unused instead of powering the model isn't very optimal. You could add this testing data to the final model, but then you have the same problem: you don't know if it was actually better off without it.

Something that can be done instead is called cross-validation . You split the training dataset randomly into equally sized sets (for example 4 sets with 25% of the data each). For each one of these sets, you train a classifier with all the data that's not in this set (75% of the data) and use this set as the gold standard to test.

Then, you build the final model by training with all the data, but the performance metrics you use for it are the average of the partial evaluations.

Topic analysis helps businesses become more efficient by saving time on repetitive manual tasks and gathers insights from the text data they manage on a daily basis.

From sales and marketing to customer support and product teams, topic analysis offers endless possibilities across different industries and areas within a company. Let’s say you want to uncover the main themes of conversations around your brand in social media, understand the priorities of hundreds of incoming support tickets, or identify brand promoters based on customer feedback. Topic analysis enables you to do all this (and more) in a simple, fast, and cost-effective way.

Getting started is easy – you don’t need a data science background or coding skills or.

How to use topic analysis for your business:

Social Media Monitoring

Brand monitoring, customer service, voice of customer (voc), business intelligence, sales and marketing, product analytics, knowledge management.

Every day, people send 500 million tweets . Impressive, right? And that’s just Twitter! Within these immense volumes of social media data, there are mentions of products and services, stories of customer experiences , and interactions between users and brands. Following these conversations is vital to get real-time actionable insights from customers, address potential issues, and anticipate crises. But getting a handle on all this data can be daunting.

Merely counting clicks, likes, and brand mentions isn’t enough anymore. Topic analysis allows you to automatically add context to social media data, to understand what people are actually saying about your brand.

Imagine you work at United Airlines. You could use topic detection to analyze what users are saying about your brand on Twitter , Facebook, and Instagram, and easily identify the most common topics in the conversation. Your customers may be alluding to functionalities, ease of use, or maybe referring to customer support issues. With topic analysis, you’ll get valuable insights like:

Understanding what people value the most about your product or service
Identifying which areas of your product or service are raising more concerns
Recognizing your pain-points, so that you can use them as opportunities for improvement

You can also use topic detection to keep an eye on your competition and track trends in your field over time.

Add an extra dimension to your data analysis, by combining topic detection with sentiment analysis , so you can get a sense of the feelings and emotions behind social media comments. Aspect-based sentiment analysis is a machine learning technique that allows you to associate specific sentiments (positive, negative, neutral) to different aspects (topics) of a product or service. In the case of United Airlines, not only would you know that most of your users are talking about your in-flight menu on Twitter, but you could also find out if they are referring to it in a negative or positive way, as well as the main keywords they are using for this topic.

Follow reactions to marketing campaigns or product releases in real time and get an accurate map of perceptions and follow them as they change over time.

Example: Trump vs Hillary, analyzing Twitter mentions during the US Elections

At MonkeyLearn, we used machine learning to analyze millions of tweets posted by users during the 2016 US elections. First, we classified tweets by topic, whether they were talking about Donald Trump or Hillary Clinton. Then, we used sentiment analysis to classify tweets as positive , negative or neutral . This allowed us to do all sorts of analysis, like extracting the most relevant keywords for the negative tweets about Trump on a particular day.

This graph shows the progression of positive, neutral and negative tweets referring to Trump and Clinton over time. The majority of tweets about both candidates are negative.

It’s not all about social media. Blogs, news outlets, review sites, and forums have a strong influence over a brand’s reputation, too. In fact, nearly 90% of consumers read at least 10 online reviews before forming an opinion about a business , and almost 60% will only use a product or service if it has four or more stars. We’ve all been there, whether it’s booking a hotel for your next holiday or downloading a new app on your cell phone, checking the reviews is an inevitable step in our decision-making process.

Real-time topic analysis allows you to keep track of your brand image ( take action in case of a crisis, or make improvements based on customer feedback), but also to monitor your competitors and detect the latest trends in your industry.

Use topic identification and analysis to get insights about your brand by detecting and tracking the different areas of your business people are discussing the most.

Then, for a deeper understanding of your data, you can perform aspect-based sentiment analysis to “opinion mine” for your customers’ feelings and emotions. You could even combine this with keyword extraction to reveal the most relevant terms used about each of the topics. Combining a number of text analysis techniques allows you to get truly fine-grained results from your data analysis, to understand why something is happening and even make predictions for the future.

Example: Analyzing Slack reviews on Capterra

With MonkeyLearn, you can create personalized, custom-built models with machine learning and train them to do the work automatically. To show you exactly how it works, we used MonkeyLearn R package to analyze thousands of Slack reviews from the product review site Capterra.

After scraping the data, we defined a series of topic classifiers that helped us detect what the text in the reviews was about pricing , UX , customer support , performance , etc. Then, we used an opinion unit extractor to divide each review into individual opinions (as some sentences may contain more than one opinion). Finally, we created a sentiment analysis model to classify them as positive, negative or neutral:

Aspect-based sentiment analysis of Slack reviews

The graphic above shows the sentiment for each of the aspects analyzed, weighted by the number of mentions. We see that the aspects users love most about Slack are ease of use , integrations , and purpose , while most of the complaints refer to performance-quality-reliability , pricing , and notifications .

It’s not enough any more, to just have an amazing product at a competitive price. Being able to deliver a great customer experience can make all the difference and help you stand out from your competitors. According to a 2017 Microsoft report , 96% of people surveyed say customer service has influenced their choice and loyalty to a brand. Plus, 56% stated that they’d stopped using a product or service due to poor customer experience.

With, perhaps hundreds or thousands of support tickets arriving at your helpdesk every day, a big part of the job in customer service consists of processing large amounts of text. First, you need to find out the subject of each ticket and tag them accordingly, and then triage tickets to the corresponding area that handles each type of issue.

Machine learning opens the door to automating this repetitive and time-consuming (not to mention horribly boring) task to save valuable time for your customer support team, allowing them to focus on what they can do best: helping customers and keeping them happy. Machine learning algorithms can be trained to sort customer support tickets (in large volumes or instantaneously, tag each ticket with the relevant topic or department, and automatically route them to the proper employee, all with no need for human interaction.

Thanks to a combination of machine learning models (not only topic labeling, but also intent classification , urgency detection, sentiment analysis, and language detection) your customer support team can:

Automatically tag customer support tickets , which you can easily do with the MonkeyLearn Zendesk integration
Automatically triage and route conversations to the most appropriate team
Automatically detect the urgency of a support ticket and prioritize accordingly
Get insights from customer support conversations

Customer feedback is a valuable source of information that provides insights into the experiences, level of satisfaction, and expectations of your customers, so you can take direct action, identify promoters and detractors, and make improvements based on feedback.

Net Promoter Score (NPS) surveys and direct customer surveys are two of the most common ways of measuring customer feedback. Gathering the information is the simple part of the process, but then comes the analysis. Fortunately, topic analysis enables teams to automatically process surveys, usually in just minutes.

NPS surveys ask a simple question to evaluate customer satisfaction:

“How likely are you to recommend us to a friend?”

The customer gives a score from 0 to 10 and, depending on the answer, the customer will be classified as promoter (9 or 10), passive (7, 8) or detractor (6 or below).

The second part is where NPS survey analysis gets tough – the respondent is asked to explain why they responded as they did. It’s an open-ended response . For example, a customer may have given a 6 on the survey and then responded:

“The product is really great, I love the UX as its really easy to use. The bad thing about the service is the pricing, it’s quite expensive”

This response provides MUCH more information, and now, with the help of machine learning topic analysis, it can be analyzed automatically. Let’s take a look at this real use case:

Retently used MonkeyLearn to analyze NPS responses . They created a topic classifier and trained it to tag each response with different topics like Product UX , Customer Support and Ease of Use . Then, they grouped the Promoters, Passives, and Detractors to determine the most prevalent tags in each group’s feedback. The result looked like this:

Combining topic analysis with sentiment analysis and keyword extraction is a powerful approach that enables you to see beyond the NPS score and really understand how your customers feel about your product, and what aspects they appreciate or criticize.

Analyzing Customer Surveys

Whether it’s emails or online surveys, if you have lots of open-ended questions to tag, machine learning can handle it! Forget about time-consuming manual tasks and get results fast and simply. MonkeyLearn even allows you to integrate directly with survey tools you may already use, for streamlined survey data analysis , like Google Forms and SurveyMonkey .

This is the era of data. Business intelligence (BI) is an all-around, holistic approach to data analysis, collecting data from as many sources as possible for historical, real time, and predictive analyses. By taking advantage of insightful and actionable information, companies are able to improve their decision-making processes, stand out from their competitors, identify trends and spot problems before they escalate.

Use topic analysis to find recurrent themes in a set of data, and obtain valuable insights about what’s important to your customers . Once you’ve done that, you can also run an aspect-based sentiment analysis to add an extra layer of analysis and gain a deeper understanding about how customers feel about each of those topics.

MonkeyLearn is more than a text analysis tool. It also provides in-depth data visualization. Once you’ve gathered all your data, and analyzed it, you’ll get a striking dashboard to share with your team. Here’s an example of a MonkeyLearn dashboard that shows the results of an analysis on Zoom reviews. Filter by sentiment, topic, keyword, and more, to see the detailed insights you can discover.

Want to see for yourself how it works? Book a demo with our team .

MonkeyLearn Studio dashboard showing results for intent classification and sentiment analysis in charts and graphs.

Lead qualification is one of the top challenges for sales teams. Only the leads that fit your ideal buyer persona can be qualified as good prospects for your product or service, and identifying them often requires tons of tedious research and manual tasks. What if you could use topic analysis to partially automate lead qualification and help make it even more effective?

Xeneta , a company that provides sea freight market intelligence, is doing exactly that. Machine learning is helping them predict the quality of their leads based on company descriptions. Basically, they trained an algorithm to do a job that used to take them dozens of hours of manual processing.

Productivity influencer Ari Meisel is also using machine learning to identify potential customers. He trained a classifier model with MonkeyLearn that analyzes e-mails enriched with publicly available data and is able to predict if a contact is a good fit for any of the services he offers.

Another exciting use case of machine learning topic detection in sales is Drift . On its mission to connect salespeople with the best leads, the company is using MonkeyLearn to automatically filter outbound email responses and manage unsubscribe requests. Thanks to natural language processing, they avoid sending unwanted emails by allowing recipients to opt out based on how they reply. That way, they save their sales team valuable time.

Intent classification is another great topic analysis method that can automatically classify email responses to marketing campaigns as, among others, Interested, Not Interested, Email Bounce, etc.

One of the main challenges for product managers tasked with improving their products is to look both at the details and the “bigger picture.” When it comes to the customer journey, for example, product managers should be able to effectively anticipate a customer’s needs and take action based on customer feedback.

Text analysis can be used to analyze customer interactions and automatically detect areas for improvement.

Let’s say you’re analyzing data from customer support interactions and you see a spike in the number of people asking how to use a new feature. This may indicate that the explanation about how to use the feature is unclear, so you need to improve the UI/UX – or any documentation about that feature.

Organizations generate a huge amount of data every day. In this context, knowledge management aims to provide the means to capture, store, retrieve, and share that data when needed. Topic detection has enormous potential when it comes to analyzing large datasets and extracting the most relevant information out of them.

This could transform industries like healthcare, where tons of complex data is produced every second – and it’s expecting to see explosive growth in the next few years – but is extremely difficult to access when needed. Topic analysis makes it possible to classify data by disease, symptoms, treatments, and more, so it can be accessed quickly, when needed, even used to find patterns and other relevant insights.

Take a look at some resources below to find more information about NLP topic detection, classification, and modeling, and text analysis overall. Then we’ll show you how easy it is to get started with topic analysis models and simple, step-by-step tutorials.

You’re probably eager to get started with topic analysis, but you may not know where to begin. The good news is that there are many useful tools and resources.

Implementing the algorithms we discussed earlier can be a great exercise to understand how they work. However, if you don’t have years of data science and coding experience, you’re probably better off sticking to SaaS solutions. SaaS text analysis tools can be implemented right away, are much less costly, and can be trained to be just as effective as building models from scratch. Take a look at The Build vs. Buy Debate to learn more.

Open source libraries

If you are going to code yourself, there are a plethora of open source libraries available in many programming languages to do topic analysis. Whether you're using topic modeling or topic classification, here are some useful pointers.

Topic analysis in Python

Python has grown in recent years to become one of the most important languages of the data science community. Easy to use, powerful, and with a great supportive community behind it, Python is ideal for getting started with machine learning and topic analysis.

Gensim is the first stop for anything related to topic modeling in Python. It has support for performing both LSA and LDA , among other topic modeling algorithms, and implementations of the most popular text vectorization algorithms.

NLTK is a library for everything NLP-related. Its main purpose is to process text: cleaning it, splitting paragraphs into sentences, splitting up words in those sentences, and so on. Since it's for working with text in general, it's useful for both topic modeling and classification. This library is not the quickest of its kind (that title probably goes to spaCy ), but it's powerful, easy to use, and comes with plenty of tidy corpora to use for training.

Scikit-learn is a simple library for everything related to machine learning in Python. For topic classification, it's a great place to start: from cleaning datasets and training models, to measuring results, scikit-learn provides tools to do it all. And, since it's built on top of NumPy and SciPy , t's quite fast, as well.

Topic analysis in R

R is a language that’s popular with the statistics crowd at the moment. Since there are a lot of statistics involved in topic analysis, it's only natural to use R to solve stat-based problems.

For topic modeling, try the topicmodels package. It's part of the tidytext family, which uses tidy data principles to make text analysis tasks easier and more effective.

Caret is a great package for doing machine learning in R. This package offers a simple API for trying out different algorithms and includes other features for topic classification such as pre-processing, feature selection, and model tuning.

mlR (short for machine learning in R ) is an alternative R package also for training and evaluating machine learning models.

Topic Analysis SaaS APIs

For non-coders, SaaS tools are definitely the way to go. Otherwise, you’d have to hire a whole team of developers, which could take months and cost in the hundreds of thousands of dollars.

SaaS APIs usually only require a few lines of code to call and most integrate with tools you already use, so you don’t need to learn whole new systems.

MonkeyLearn offers a suite of SaaS topic analysis and many more text analysis tools that can be called with just 10 lines of code and custom-tailored to the language and needs of your business, usually in just a few minutes.

Try out the Intent and Email Classifier , for example, that’s pre-trained to understand the reason behind email responses and classify them into topics: Autoresponder, Email Bounce, Interested, Not Interested, Unsubscribe, or Wrong Person.

Or take a look at other pre-trained text analysis models below see how they work:

Keyword Extractor : find the most used and most important keywords in your own text
Sentiment Analyzer: classify text by opinion polarity (positive, negative, neutral)
Word Cloud Generator : a keyword clustering tool that groups keywords by size, according to their importance within the text

The MonkeyLearn API offers simple SDKs for pre-trained models and tutorials to teach you how to train your own. And with MonkeyLearn Studio you can chain together all the analyses you need and have them work in concert, automatically, then visualize your results. It all works in a single dashboard, so you no longer have to upload and download between applications.

Other great SaaS topic analysis solutions:

Amazon Comprehend
Google Cloud NLP
MeaningCloud

Papers About Topic Modeling and Topic Classification

If you would like to learn more about the finer details of how topic analysis works the following papers are a good starting point:

An Introduction to Latent Semantic Analysis (Landauer, Foltz and Laham, D., 1998)
Indexing by Latent Semantic Analysis (Deerwester et al., 1990)
Latent Dirichlet Allocation (Blei, Ng and Jordan, 2003)
An empirical study of the naive Bayes classifier (Rish, 2001)
Text categorization with Support Vector Machines: Learning with many relevant features (Joachims, 1998)

There are online courses for students at any stage of their topic analysis journey.

For an explanation of topic modeling in general, check out this lecture from the University of Michigan's text mining course in Coursera. Here, you can also find this lecture covering text classification. Plus, this course at Udemy covers NLP in general and several aspects of topic modeling as well.

If you are looking for lectures on some of the algorithms covered in this piece, check out:

Naive Bayes , explained by Andrew Ng.
Support Vector Machines , a visual explanation with sample code.
Deep Learning explained simply in four parts. This series is amazing for getting a sense of the idea behind deep learning.
Latent Dirichlet Allocation explained in a simple and understandable way. For a more in-depth dive, try this lecture by David Blei, author of the seminal LDA paper.

Now, if what you're interested in is a pro-level course in machine learning, Stanford cs229 is a must. It's an advanced course for computer science students, so it's rated M for Math (which is great if that's what you're into). All the material is available online for free, so you can just hop in and check it out at your own pace.

Topic Analysis Tutorials

By now, you’re probably ready to dive in and make your own model. This section is split into two different parts. First, we’ll provide step-by-step tutorials to build topic analysis models using Python (with Gensim and NLTK) and R.

Then, we’ll show you how to build a classifier for topic analysis using MonkeyLearn.

Tutorials Using Open Source Libraries

Topic classification in python.

For this, we will cover a simple example of creating a text classifier using NLTK and scikit-learn .

Download a CSV with sample data for this classifier here.

This is a list of lists, representing the rows and columns of the CSV file. Every row in the CSV has three columns: the text, the sentiment for that text (we won't use that one in this tutorial) and the topic:

First, let's do a bit of processing and cleaning of the data. When doing NLP, text cleanup and processing is a very important first step. Good models cannot be trained with dirty data.

For this purpose, we define a function to do the processing using NLTK. An important feature of this library is that it comes with several corpora ready to use. Since they can get pretty big, they aren't included with the library; they have to be downloaded with the download function .

Now that we have that function ready, process the data:

The sentences turned into list of stemmed words without any connectors, which is what we need to feed the algorithm. texts looks like this now:

With the cleanup out of the way, we are ready to start training. First, the texts must be vectorized, that is, transformed into numbers that we can feed to the machine learning algorithm.

We do this using scikit-learn.

Now, we separate our training data and our test data, in order to obtain performance metrics .

Finally, we train a Naive Bayes classifier with the training set and test the model using the testing set.

This outputs something like this:

It's not a stellar performance, but considering the size of the dataset it's not bad. If you don't know what precision , recall , and f1-score are, they're explained in the Metrics and Evaluation section . Support for a category is simply how many samples there were in that category.

From here, the model can be tweaked and tested again in order to get better results. A good starting point is the parameters of the CountVectorizer .

Of course, this is a very simple example, but it illustrates all the steps required for building a classifier: obtain the data, clean it, process it, train a model, and iterate.

Using this same process you can also train a classifier for sentiment analysis , with the sentiment tags included in the dataset that we didn't use in this tutorial.

Topic Modeling in Python

For topic modeling we will use Gensim .

We'll be building on the preprocessing done on the previous tutorial, so we just need to worry about getting Gensim up and running:

We pick up halfway through the classifier tutorial. We leave our text as a list of words, since Gensim accepts that as input. Then, we create a Gensim dictionary from the data using the bag of words model:

After that, we're ready to go. It's important to note that here we're just using the review texts, and not the topics that come with the dataset. Using this dictionary, we train an LDA model, instructing Gensim to find three topics in the data:

And that's it! The code will print out the mixture of the most representative words for three topics:

Interestingly, the algorithm identified words that look a lot like keywords for our original Facilities , Comfort and Cleanliness topics.

Since this is a toy example with few texts (and we know their topic) it isn't very useful, but this example illustrates the basics of how to do topic modeling using Gensim.

Topic Modeling in R

If you want to do topic modeling in R, we urge you to try out the Tidy Topic Modeling tutorial for the topicmodels package . It's straightforward and explains the basics for doing topic modeling using R.

Using MonkeyLearn Templates

With MonkeyLearn , you can gain access to our templates, which are really easy to use if you’re looking for a completely code-free experience.

Here’s how our templates work:

1. Choose your template

For this quick walkthrough of how to use our templates, we’ve selected the NPS analysis template.

2. Upload your data:

If you don't have a CSV file:

You can use our sample dataset .
Or, download your own survey responses from the survey tool you use with this documentation .

3. Match your data to the right fields in each column:

Fields you'll need to match:

created_at: Date that the response was sent.
text: Text of the response.
score: NPS score given by the customer.

4. Name your workflow:

5. Wait for MonkeyLearn to process your data:

6. Explore your dashboard!

Filter by topic, sentiment, keyword, score, or NPS category.
Share via email with other coworkers.

Want to run your data through MonkeyLearn templates? Book a demo .

Final Words on Topic Analysis

Topic analysis makes it possible to detect topics and subjects within huge sets of text data in a fast and simple way. Topic classification allows you to automate your business, from customer service to social media monitoring and beyond. Thanks to topic analysis, you can accomplish complex tasks more effectively and obtain valuable insights from your data that will lead to better business decisions.

Or book a demo to learn more about the insights you can get from your data.

Research Paper Analysis: How to Analyze a Research Article + Example

Why might you need to analyze research? First of all, when you analyze a research article, you begin to understand your assigned reading better. It is also the first step toward learning how to write your own research articles and literature reviews. However, if you have never written a research paper before, it may be difficult for you to analyze one. After all, you may not know what criteria to use to evaluate it. But don’t panic! We will help you figure it out!

In this article, our team has explained how to analyze research papers quickly and effectively. At the end, you will also find a research analysis paper example to see how everything works in practice.

🔤 Research Analysis Definition

📊 How to Analyze a Research Article

✍️ how to write a research analysis.

📝 Analysis Example
🔎 More Examples

🔗 References

🔤 research paper analysis: what is it.

A research paper analysis is an academic writing assignment in which you analyze a scholarly article’s methodology, data, and findings. In essence, “to analyze” means to break something down into components and assess each of them individually and in relation to each other. The goal of an analysis is to gain a deeper understanding of a subject. So, when you analyze a research article, you dissect it into elements like data sources , research methods, and results and evaluate how they contribute to the study’s strengths and weaknesses.

📋 Research Analysis Format

A research analysis paper has a pretty straightforward structure. Check it out below!

Research articles usually include the following sections: introduction, methods, results, and discussion. In the following paragraphs, we will discuss how to analyze a scientific article with a focus on each of its parts.

This image shows the main sections of a research article.

How to Analyze a Research Paper: Purpose

The purpose of the study is usually outlined in the introductory section of the article. Analyzing the research paper’s objectives is critical to establish the context for the rest of your analysis.

When analyzing the research aim, you should evaluate whether it was justified for the researchers to conduct the study. In other words, you should assess whether their research question was significant and whether it arose from existing literature on the topic.

Here are some questions that may help you analyze a research paper’s purpose:

Why was the research carried out?
What gaps does it try to fill, or what controversies to settle?
How does the study contribute to its field?
Do you agree with the author’s justification for approaching this particular question in this way?

How to Analyze a Paper: Methods

When analyzing the methodology section , you should indicate the study’s research design (qualitative, quantitative, or mixed) and methods used (for example, experiment, case study, correlational research, survey, etc.). After that, you should assess whether these methods suit the research purpose. In other words, do the chosen methods allow scholars to answer their research questions within the scope of their study?

For example, if scholars wanted to study US students’ average satisfaction with their higher education experience, they could conduct a quantitative survey . However, if they wanted to gain an in-depth understanding of the factors influencing US students’ satisfaction with higher education, qualitative interviews would be more appropriate.

When analyzing methods, you should also look at the research sample . Did the scholars use randomization to select study participants? Was the sample big enough for the results to be generalizable to a larger population?

You can also answer the following questions in your methodology analysis:

Is the methodology valid? In other words, did the researchers use methods that accurately measure the variables of interest?
Is the research methodology reliable? A research method is reliable if it can produce stable and consistent results under the same circumstances.
Is the study biased in any way?
What are the limitations of the chosen methodology?

How to Analyze Research Articles’ Results

You should start the analysis of the article results by carefully reading the tables, figures, and text. Check whether the findings correspond to the initial research purpose. See whether the results answered the author’s research questions or supported the hypotheses stated in the introduction.

To analyze the results section effectively, answer the following questions:

What are the major findings of the study?
Did the author present the results clearly and unambiguously?
Are the findings statistically significant ?
Does the author provide sufficient information on the validity and reliability of the results?
Have you noticed any trends or patterns in the data that the author did not mention?

How to Analyze Research: Discussion

Finally, you should analyze the authors’ interpretation of results and its connection with research objectives. Examine what conclusions the authors drew from their study and whether these conclusions answer the original question.

You should also pay attention to how the authors used findings to support their conclusions. For example, you can reflect on why their findings support that particular inference and not another one. Moreover, more than one conclusion can sometimes be made based on the same set of results. If that’s the case with your article, you should analyze whether the authors addressed other interpretations of their findings .

Here are some useful questions you can use to analyze the discussion section:

What findings did the authors use to support their conclusions?
How do the researchers’ conclusions compare to other studies’ findings?
How does this study contribute to its field?
What future research directions do the authors suggest?
What additional insights can you share regarding this article? For example, do you agree with the results? What other questions could the researchers have answered?

This image shows how to analyze a research article.

Now, you know how to analyze an article that presents research findings. However, it’s just a part of the work you have to do to complete your paper. So, it’s time to learn how to write research analysis! Check out the steps below!

1. Introduce the Article

As with most academic assignments, you should start your research article analysis with an introduction. Here’s what it should include:

The article’s publication details . Specify the title of the scholarly work you are analyzing, its authors, and publication date. Remember to enclose the article’s title in quotation marks and write it in title case .
The article’s main point . State what the paper is about. What did the authors study, and what was their major finding?
Your thesis statement . End your introduction with a strong claim summarizing your evaluation of the article. Consider briefly outlining the research paper’s strengths, weaknesses, and significance in your thesis.

Keep your introduction brief. Save the word count for the “meat” of your paper — that is, for the analysis.

2. Summarize the Article

Now, you should write a brief and focused summary of the scientific article. It should be shorter than your analysis section and contain all the relevant details about the research paper.

Here’s what you should include in your summary:

The research purpose . Briefly explain why the research was done. Identify the authors’ purpose and research questions or hypotheses .
Methods and results . Summarize what happened in the study. State only facts, without the authors’ interpretations of them. Avoid using too many numbers and details; instead, include only the information that will help readers understand what happened.
The authors’ conclusions . Outline what conclusions the researchers made from their study. In other words, describe how the authors explained the meaning of their findings.

If you need help summarizing an article, you can use our free summary generator .

3. Write Your Research Analysis

The analysis of the study is the most crucial part of this assignment type. Its key goal is to evaluate the article critically and demonstrate your understanding of it.

We’ve already covered how to analyze a research article in the section above. Here’s a quick recap:

Analyze whether the study’s purpose is significant and relevant.
Examine whether the chosen methodology allows for answering the research questions.
Evaluate how the authors presented the results.
Assess whether the authors’ conclusions are grounded in findings and answer the original research questions.

Although you should analyze the article critically, it doesn’t mean you only should criticize it. If the authors did a good job designing and conducting their study, be sure to explain why you think their work is well done. Also, it is a great idea to provide examples from the article to support your analysis.

4. Conclude Your Analysis of Research Paper

A conclusion is your chance to reflect on the study’s relevance and importance. Explain how the analyzed paper can contribute to the existing knowledge or lead to future research. Also, you need to summarize your thoughts on the article as a whole. Avoid making value judgments — saying that the paper is “good” or “bad.” Instead, use more descriptive words and phrases such as “This paper effectively showed…”

Need help writing a compelling conclusion? Try our free essay conclusion generator !

5. Revise and Proofread

Last but not least, you should carefully proofread your paper to find any punctuation, grammar, and spelling mistakes. Start by reading your work out loud to ensure that your sentences fit together and sound cohesive. Also, it can be helpful to ask your professor or peer to read your work and highlight possible weaknesses or typos.

This image shows how to write a research analysis.

📝 Research Paper Analysis Example

We have prepared an analysis of a research paper example to show how everything works in practice.

No Homework Policy: Research Article Analysis Example

This paper aims to analyze the research article entitled “No Assignment: A Boon or a Bane?” by Cordova, Pagtulon-an, and Tan (2019). This study examined the effects of having and not having assignments on weekends on high school students’ performance and transmuted mean scores. This article effectively shows the value of homework for students, but larger studies are needed to support its findings.

Cordova et al. (2019) conducted a descriptive quantitative study using a sample of 115 Grade 11 students of the Central Mindanao University Laboratory High School in the Philippines. The sample was divided into two groups: the first received homework on weekends, while the second didn’t. The researchers compared students’ performance records made by teachers and found that students who received assignments performed better than their counterparts without homework.

The purpose of this study is highly relevant and justified as this research was conducted in response to the debates about the “No Homework Policy” in the Philippines. Although the descriptive research design used by the authors allows to answer the research question, the study could benefit from an experimental design. This way, the authors would have firm control over variables. Additionally, the study’s sample size was not large enough for the findings to be generalized to a larger population.

The study results are presented clearly, logically, and comprehensively and correspond to the research objectives. The researchers found that students’ mean grades decreased in the group without homework and increased in the group with homework. Based on these findings, the authors concluded that homework positively affected students’ performance. This conclusion is logical and grounded in data.

This research effectively showed the importance of homework for students’ performance. Yet, since the sample size was relatively small, larger studies are needed to ensure the authors’ conclusions can be generalized to a larger population.

🔎 More Research Analysis Paper Examples

Do you want another research analysis example? Check out the best analysis research paper samples below:

Gracious Leadership Principles for Nurses: Article Analysis
Effective Mental Health Interventions: Analysis of an Article
Nursing Turnover: Article Analysis
Nursing Practice Issue: Qualitative Research Article Analysis
Quantitative Article Critique in Nursing
LIVE Program: Quantitative Article Critique
Evidence-Based Practice Beliefs and Implementation: Article Critique
“Differential Effectiveness of Placebo Treatments”: Research Paper Analysis
“Family-Based Childhood Obesity Prevention Interventions”: Analysis Research Paper Example
“Childhood Obesity Risk in Overweight Mothers”: Article Analysis
“Fostering Early Breast Cancer Detection” Article Analysis
Lesson Planning for Diversity: Analysis of an Article
Journal Article Review: Correlates of Physical Violence at School
Space and the Atom: Article Analysis
“Democracy and Collective Identity in the EU and the USA”: Article Analysis
China’s Hegemonic Prospects: Article Review
Article Analysis: Fear of Missing Out
Article Analysis: “Perceptions of ADHD Among Diagnosed Children and Their Parents”
Codependence, Narcissism, and Childhood Trauma: Analysis of the Article
Relationship Between Work Intensity, Workaholism, Burnout, and MSC: Article Review

We hope that our article on research paper analysis has been helpful. If you liked it, please share this article with your friends!

Analyzing Research Articles: A Guide for Readers and Writers | Sam Mathews
Summary and Analysis of Scientific Research Articles | San José State University Writing Center
Analyzing Scholarly Articles | Texas A&M University
Article Analysis Assignment | University of Wisconsin-Madison
How to Summarize a Research Article | University of Connecticut
Critique/Review of Research Articles | University of Calgary
Art of Reading a Journal Article: Methodically and Effectively | PubMed Central
Write a Critical Review of a Scientific Journal Article | McLaughlin Library
How to Read and Understand a Scientific Paper: A Guide for Non-scientists | LSE
How to Analyze Journal Articles | Classroom

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"Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts." Source: Columbia Public Health
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Content analysis. Content analysis is a research method in the social sciences used to reduce large amounts of unstructured textual content into manageable data relevant to the (evaluation) research questions. Texts refer to any occurrence of communications - including websites, social media, books, essays, interviews, focus groups, diaries ...
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What Is Topic Analysis? Topic analysis (also called topic detection, topic modeling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning "tags" or categories according to each individual text's topic or theme.. Topic analysis uses natural language processing (NLP) to break down human language so that you can ...
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3. In their study on social work research, Mackieson, Shlonsky, and Connolly (2019) discuss thematic analysis. Focusing on a document analysis of Canadian parliamentary debates, Table 1 provides a sample code definition for a high-level theme (FAMILY-FIRST). Table 2 provides a sample code definition for a sub-theme (FAM-NUCLEAR).
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Research Paper Analysis: How to Analyze a Research Article + Example
Save the word count for the "meat" of your paper — that is, for the analysis. 2. Summarize the Article. Now, you should write a brief and focused summary of the scientific article. It should be shorter than your analysis section and contain all the relevant details about the research paper.

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Content Analysis | A Step-by-Step Guide with Examples

Table of contents

Prevent plagiarism, run a free check.

Step 1: Select the content you will analyse

Step 2: Define the units and categories of analysis

Step 3: Develop a set of rules for coding

Step 4: Code the text according to the rules

Step 5: Analyse the results and draw conclusions

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Chapter 17. Content Analysis

An Overview of the Content Analysis Process

The Second Step: Collecting and Storing

The Third Step: Analysis

The Fourth and Final Step: The Write-Up

Analyzing Content: Helpful Hints and Pointers

Further Readings

Using Content Analysis

An Introduction to Content Analysis

A Brief History of Content Analysis

Uses of Content Analysis

Types of Content Analysis

Conceptual Analysis

Methods of Conceptual Analysis

Steps for Conducting Conceptual Analysis

Relational Analysis

Theoretical Influences on Relational Analysis

Relational Analysis: Overview of Methods

Three Subcategories of Relational Analysis

Steps for Conducting Relational Analysis

Issues of Reliability & Validity

Advantages of Content Analysis

Disadvantages of Content Analysis

Annotated Bibliography

What is Content Analysis – Steps & Examples

The Purpose of Content Analysis

When to Use Content Analysis?

Types of Content Analysis

Confused between qualitative and quantitative methods of data analysis? No idea what discourse and content analysis are?

Advantages and Disadvantages of Content Analysis

Disadvantages

How to Conduct a Content Analysis?

Develop a Research Question and Select the Content

Could you read it Thoroughly?

Condensation

Code the Content

Analyse and Interpret the Data

Frequently Asked Questions

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18.5 Content analysis

What are you trying to accomplish with content analysis

Variations in the approach

Determining your codes

Coding, comparing, counting

Representing the findings from your coding scheme

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