how to write an analysis of a text

beginner's guide to literary analysis

Understanding literature & how to write literary analysis.

Literary analysis is the foundation of every college and high school English class. Once you can comprehend written work and respond to it, the next step is to learn how to think critically and complexly about a work of literature in order to analyze its elements and establish ideas about its meaning.

If that sounds daunting, it shouldn’t. Literary analysis is really just a way of thinking creatively about what you read. The practice takes you beyond the storyline and into the motives behind it. 

While an author might have had a specific intention when they wrote their book, there’s still no right or wrong way to analyze a literary text—just your way. You can use literary theories, which act as “lenses” through which you can view a text. Or you can use your own creativity and critical thinking to identify a literary device or pattern in a text and weave that insight into your own argument about the text’s underlying meaning. 

Now, if that sounds fun, it should , because it is. Here, we’ll lay the groundwork for performing literary analysis, including when writing analytical essays, to help you read books like a critic. 

What Is Literary Analysis?

As the name suggests, literary analysis is an analysis of a work, whether that’s a novel, play, short story, or poem. Any analysis requires breaking the content into its component parts and then examining how those parts operate independently and as a whole. In literary analysis, those parts can be different devices and elements—such as plot, setting, themes, symbols, etcetera—as well as elements of style, like point of view or tone. 

When performing analysis, you consider some of these different elements of the text and then form an argument for why the author chose to use them. You can do so while reading and during class discussion, but it’s particularly important when writing essays. 

Literary analysis is notably distinct from summary. When you write a summary , you efficiently describe the work’s main ideas or plot points in order to establish an overview of the work. While you might use elements of summary when writing analysis, you should do so minimally. You can reference a plot line to make a point, but it should be done so quickly so you can focus on why that plot line matters . In summary (see what we did there?), a summary focuses on the “ what ” of a text, while analysis turns attention to the “ how ” and “ why .”

While literary analysis can be broad, covering themes across an entire work, it can also be very specific, and sometimes the best analysis is just that. Literary critics have written thousands of words about the meaning of an author’s single word choice; while you might not want to be quite that particular, there’s a lot to be said for digging deep in literary analysis, rather than wide. 

Although you’re forming your own argument about the work, it’s not your opinion . You should avoid passing judgment on the piece and instead objectively consider what the author intended, how they went about executing it, and whether or not they were successful in doing so. Literary criticism is similar to literary analysis, but it is different in that it does pass judgement on the work. Criticism can also consider literature more broadly, without focusing on a singular work. 

Once you understand what constitutes (and doesn’t constitute) literary analysis, it’s easy to identify it. Here are some examples of literary analysis and its oft-confused counterparts: 

Summary: In “The Fall of the House of Usher,” the narrator visits his friend Roderick Usher and witnesses his sister escape a horrible fate.  

Opinion: In “The Fall of the House of Usher,” Poe uses his great Gothic writing to establish a sense of spookiness that is enjoyable to read. 

Literary Analysis: “Throughout ‘The Fall of the House of Usher,’ Poe foreshadows the fate of Madeline by creating a sense of claustrophobia for the reader through symbols, such as in the narrator’s inability to leave and the labyrinthine nature of the house. 

In summary, literary analysis is:

• Breaking a work into its components
• Identifying what those components are and how they work in the text
• Developing an understanding of how they work together to achieve a goal 
• Not an opinion, but subjective 
• Not a summary, though summary can be used in passing 
• Best when it deeply, rather than broadly, analyzes a literary element

Literary Analysis and Other Works

As discussed above, literary analysis is often performed upon a single work—but it doesn’t have to be. It can also be performed across works to consider the interplay of two or more texts. Regardless of whether or not the works were written about the same thing, or even within the same time period, they can have an influence on one another or a connection that’s worth exploring. And reading two or more texts side by side can help you to develop insights through comparison and contrast.

For example, Paradise Lost is an epic poem written in the 17th century, based largely on biblical narratives written some 700 years before and which later influenced 19th century poet John Keats. The interplay of works can be obvious, as here, or entirely the inspiration of the analyst. As an example of the latter, you could compare and contrast the writing styles of Ralph Waldo Emerson and Edgar Allan Poe who, while contemporaries in terms of time, were vastly different in their content. 

Additionally, literary analysis can be performed between a work and its context. Authors are often speaking to the larger context of their times, be that social, political, religious, economic, or artistic. A valid and interesting form is to compare the author’s context to the work, which is done by identifying and analyzing elements that are used to make an argument about the writer’s time or experience. 

For example, you could write an essay about how Hemingway’s struggles with mental health and paranoia influenced his later work, or how his involvement in the Spanish Civil War influenced his early work. One approach focuses more on his personal experience, while the other turns to the context of his times—both are valid. 

Why Does Literary Analysis Matter? 

Sometimes an author wrote a work of literature strictly for entertainment’s sake, but more often than not, they meant something more. Whether that was a missive on world peace, commentary about femininity, or an allusion to their experience as an only child, the author probably wrote their work for a reason, and understanding that reason—or the many reasons—can actually make reading a lot more meaningful. 

Performing literary analysis as a form of study unquestionably makes you a better reader. It’s also likely that it will improve other skills, too, like critical thinking, creativity, debate, and reasoning. 

At its grandest and most idealistic, literary analysis even has the ability to make the world a better place. By reading and analyzing works of literature, you are able to more fully comprehend the perspectives of others. Cumulatively, you’ll broaden your own perspectives and contribute more effectively to the things that matter to you. 

Literary Terms to Know for Literary Analysis 

There are hundreds of literary devices you could consider during your literary analysis, but there are some key tools most writers utilize to achieve their purpose—and therefore you need to know in order to understand that purpose. These common devices include: 

• Characters: The people (or entities) who play roles in the work. The protagonist is the main character in the work. 
• Conflict: The conflict is the driving force behind the plot, the event that causes action in the narrative, usually on the part of the protagonist
• Context : The broader circumstances surrounding the work political and social climate in which it was written or the experience of the author. It can also refer to internal context, and the details presented by the narrator 
• Diction : The word choice used by the narrator or characters 
• Genre: A category of literature characterized by agreed upon similarities in the works, such as subject matter and tone
• Imagery : The descriptive or figurative language used to paint a picture in the reader’s mind so they can picture the story’s plot, characters, and setting 
• Metaphor: A figure of speech that uses comparison between two unlike objects for dramatic or poetic effect
• Narrator: The person who tells the story. Sometimes they are a character within the story, but sometimes they are omniscient and removed from the plot. 
• Plot : The storyline of the work
• Point of view: The perspective taken by the narrator, which skews the perspective of the reader 
• Setting : The time and place in which the story takes place. This can include elements like the time period, weather, time of year or day, and social or economic conditions 
• Symbol : An object, person, or place that represents an abstract idea that is greater than its literal meaning 
• Syntax : The structure of a sentence, either narration or dialogue, and the tone it implies
• Theme : A recurring subject or message within the work, often commentary on larger societal or cultural ideas
• Tone : The feeling, attitude, or mood the text presents

How to Perform Literary Analysis

Step 1: read the text thoroughly.

Literary analysis begins with the literature itself, which means performing a close reading of the text. As you read, you should focus on the work. That means putting away distractions (sorry, smartphone) and dedicating a period of time to the task at hand. 

It’s also important that you don’t skim or speed read. While those are helpful skills, they don’t apply to literary analysis—or at least not this stage. 

Step 2: Take Notes as You Read  

As you read the work, take notes about different literary elements and devices that stand out to you. Whether you highlight or underline in text, use sticky note tabs to mark pages and passages, or handwrite your thoughts in a notebook, you should capture your thoughts and the parts of the text to which they correspond. This—the act of noticing things about a literary work—is literary analysis. 

Step 3: Notice Patterns 

As you read the work, you’ll begin to notice patterns in the way the author deploys language, themes, and symbols to build their plot and characters. As you read and these patterns take shape, begin to consider what they could mean and how they might fit together. 

As you identify these patterns, as well as other elements that catch your interest, be sure to record them in your notes or text. Some examples include: 

• Circle or underline words or terms that you notice the author uses frequently, whether those are nouns (like “eyes” or “road”) or adjectives (like “yellow” or “lush”).
• Highlight phrases that give you the same kind of feeling. For example, if the narrator describes an “overcast sky,” a “dreary morning,” and a “dark, quiet room,” the words aren’t the same, but the feeling they impart and setting they develop are similar. 
• Underline quotes or prose that define a character’s personality or their role in the text.
• Use sticky tabs to color code different elements of the text, such as specific settings or a shift in the point of view. 

By noting these patterns, comprehensive symbols, metaphors, and ideas will begin to come into focus.  

Step 4: Consider the Work as a Whole, and Ask Questions

This is a step that you can do either as you read, or after you finish the text. The point is to begin to identify the aspects of the work that most interest you, and you could therefore analyze in writing or discussion. 

Questions you could ask yourself include: 

• What aspects of the text do I not understand?
• What parts of the narrative or writing struck me most?
• What patterns did I notice?
• What did the author accomplish really well?
• What did I find lacking?
• Did I notice any contradictions or anything that felt out of place?  
• What was the purpose of the minor characters?
• What tone did the author choose, and why? 

The answers to these and more questions will lead you to your arguments about the text. 

Step 5: Return to Your Notes and the Text for Evidence

As you identify the argument you want to make (especially if you’re preparing for an essay), return to your notes to see if you already have supporting evidence for your argument. That’s why it’s so important to take notes or mark passages as you read—you’ll thank yourself later!

If you’re preparing to write an essay, you’ll use these passages and ideas to bolster your argument—aka, your thesis. There will likely be multiple different passages you can use to strengthen multiple different aspects of your argument. Just be sure to cite the text correctly! 

If you’re preparing for class, your notes will also be invaluable. When your teacher or professor leads the conversation in the direction of your ideas or arguments, you’ll be able to not only proffer that idea but back it up with textual evidence. That’s an A+ in class participation. 

Step 6: Connect These Ideas Across the Narrative

Whether you’re in class or writing an essay, literary analysis isn’t complete until you’ve considered the way these ideas interact and contribute to the work as a whole. You can find and present evidence, but you still have to explain how those elements work together and make up your argument. 

How to Write a Literary Analysis Essay

When conducting literary analysis while reading a text or discussing it in class, you can pivot easily from one argument to another (or even switch sides if a classmate or teacher makes a compelling enough argument). 

But when writing literary analysis, your objective is to propose a specific, arguable thesis and convincingly defend it. In order to do so, you need to fortify your argument with evidence from the text (and perhaps secondary sources) and an authoritative tone. 

A successful literary analysis essay depends equally on a thoughtful thesis, supportive analysis, and presenting these elements masterfully. We’ll review how to accomplish these objectives below. 

Step 1: Read the Text. Maybe Read It Again. 

Constructing an astute analytical essay requires a thorough knowledge of the text. As you read, be sure to note any passages, quotes, or ideas that stand out. These could serve as the future foundation of your thesis statement. Noting these sections now will help you when you need to gather evidence. 

The more familiar you become with the text, the better (and easier!) your essay will be. Familiarity with the text allows you to speak (or in this case, write) to it confidently. If you only skim the book, your lack of rich understanding will be evident in your essay. Alternatively, if you read the text closely—especially if you read it more than once, or at least carefully revisit important passages—your own writing will be filled with insight that goes beyond a basic understanding of the storyline. 

Step 2: Brainstorm Potential Topics 

Because you took detailed notes while reading the text, you should have a list of potential topics at the ready. Take time to review your notes, highlighting any ideas or questions you had that feel interesting. You should also return to the text and look for any passages that stand out to you. 

When considering potential topics, you should prioritize ideas that you find interesting. It won’t only make the whole process of writing an essay more fun, your enthusiasm for the topic will probably improve the quality of your argument, and maybe even your writing. Just like it’s obvious when a topic interests you in a conversation, it’s obvious when a topic interests the writer of an essay (and even more obvious when it doesn’t). 

Your topic ideas should also be specific, unique, and arguable. A good way to think of topics is that they’re the answer to fairly specific questions. As you begin to brainstorm, first think of questions you have about the text. Questions might focus on the plot, such as: Why did the author choose to deviate from the projected storyline? Or why did a character’s role in the narrative shift? Questions might also consider the use of a literary device, such as: Why does the narrator frequently repeat a phrase or comment on a symbol? Or why did the author choose to switch points of view each chapter? 

Once you have a thesis question , you can begin brainstorming answers—aka, potential thesis statements . At this point, your answers can be fairly broad. Once you land on a question-statement combination that feels right, you’ll then look for evidence in the text that supports your answer (and helps you define and narrow your thesis statement). 

For example, after reading “ The Fall of the House of Usher ,” you might be wondering, Why are Roderick and Madeline twins?, Or even: Why does their relationship feel so creepy?” Maybe you noticed (and noted) that the narrator was surprised to find out they were twins, or perhaps you found that the narrator’s tone tended to shift and become more anxious when discussing the interactions of the twins.

Once you come up with your thesis question, you can identify a broad answer, which will become the basis for your thesis statement. In response to the questions above, your answer might be, “Poe emphasizes the close relationship of Roderick and Madeline to foreshadow that their deaths will be close, too.” 

Step 3: Gather Evidence 

Once you have your topic (or you’ve narrowed it down to two or three), return to the text (yes, again) to see what evidence you can find to support it. If you’re thinking of writing about the relationship between Roderick and Madeline in “The Fall of the House of Usher,” look for instances where they engaged in the text. 

This is when your knowledge of literary devices comes in clutch. Carefully study the language around each event in the text that might be relevant to your topic. How does Poe’s diction or syntax change during the interactions of the siblings? How does the setting reflect or contribute to their relationship? What imagery or symbols appear when Roderick and Madeline are together? 

By finding and studying evidence within the text, you’ll strengthen your topic argument—or, just as valuably, discount the topics that aren’t strong enough for analysis. 

Step 4: Consider Secondary Sources 

In addition to returning to the literary work you’re studying for evidence, you can also consider secondary sources that reference or speak to the work. These can be articles from journals you find on JSTOR, books that consider the work or its context, or articles your teacher shared in class. 

While you can use these secondary sources to further support your idea, you should not overuse them. Make sure your topic remains entirely differentiated from that presented in the source. 

Step 5: Write a Working Thesis Statement

Once you’ve gathered evidence and narrowed down your topic, you’re ready to refine that topic into a thesis statement. As you continue to outline and write your paper, this thesis statement will likely change slightly, but this initial draft will serve as the foundation of your essay. It’s like your north star: Everything you write in your essay is leading you back to your thesis. 

Writing a great thesis statement requires some real finesse. A successful thesis statement is: 

• Debatable : You shouldn’t simply summarize or make an obvious statement about the work. Instead, your thesis statement should take a stand on an issue or make a claim that is open to argument. You’ll spend your essay debating—and proving—your argument. 
• Demonstrable : You need to be able to prove, through evidence, that your thesis statement is true. That means you have to have passages from the text and correlative analysis ready to convince the reader that you’re right. 
• Specific : In most cases, successfully addressing a theme that encompasses a work in its entirety would require a book-length essay. Instead, identify a thesis statement that addresses specific elements of the work, such as a relationship between characters, a repeating symbol, a key setting, or even something really specific like the speaking style of a character. 

Example: By depicting the relationship between Roderick and Madeline to be stifling and almost otherworldly in its closeness, Poe foreshadows both Madeline’s fate and Roderick’s inability to choose a different fate for himself. 

Step 6: Write an Outline 

You have your thesis, you have your evidence—but how do you put them together? A great thesis statement (and therefore a great essay) will have multiple arguments supporting it, presenting different kinds of evidence that all contribute to the singular, main idea presented in your thesis. 

Review your evidence and identify these different arguments, then organize the evidence into categories based on the argument they support. These ideas and evidence will become the body paragraphs of your essay. 

For example, if you were writing about Roderick and Madeline as in the example above, you would pull evidence from the text, such as the narrator’s realization of their relationship as twins; examples where the narrator’s tone of voice shifts when discussing their relationship; imagery, like the sounds Roderick hears as Madeline tries to escape; and Poe’s tendency to use doubles and twins in his other writings to create the same spooky effect. All of these are separate strains of the same argument, and can be clearly organized into sections of an outline. 

Step 7: Write Your Introduction

Your introduction serves a few very important purposes that essentially set the scene for the reader: 

• Establish context. Sure, your reader has probably read the work. But you still want to remind them of the scene, characters, or elements you’ll be discussing. 
• Present your thesis statement. Your thesis statement is the backbone of your analytical paper. You need to present it clearly at the outset so that the reader understands what every argument you make is aimed at. 
• Offer a mini-outline. While you don’t want to show all your cards just yet, you do want to preview some of the evidence you’ll be using to support your thesis so that the reader has a roadmap of where they’re going. 

Step 8: Write Your Body Paragraphs

Thanks to steps one through seven, you’ve already set yourself up for success. You have clearly outlined arguments and evidence to support them. Now it’s time to translate those into authoritative and confident prose. 

When presenting each idea, begin with a topic sentence that encapsulates the argument you’re about to make (sort of like a mini-thesis statement). Then present your evidence and explanations of that evidence that contribute to that argument. Present enough material to prove your point, but don’t feel like you necessarily have to point out every single instance in the text where this element takes place. For example, if you’re highlighting a symbol that repeats throughout the narrative, choose two or three passages where it is used most effectively, rather than trying to squeeze in all ten times it appears. 

While you should have clearly defined arguments, the essay should still move logically and fluidly from one argument to the next. Try to avoid choppy paragraphs that feel disjointed; every idea and argument should feel connected to the last, and, as a group, connected to your thesis. A great way to connect the ideas from one paragraph to the next is with transition words and phrases, such as: 

• Furthermore 
• In addition
• On the other hand
• Conversely 

Step 9: Write Your Conclusion 

Your conclusion is more than a summary of your essay's parts, but it’s also not a place to present brand new ideas not already discussed in your essay. Instead, your conclusion should return to your thesis (without repeating it verbatim) and point to why this all matters. If writing about the siblings in “The Fall of the House of Usher,” for example, you could point out that the utilization of twins and doubles is a common literary element of Poe’s work that contributes to the definitive eeriness of Gothic literature. 

While you might speak to larger ideas in your conclusion, be wary of getting too macro. Your conclusion should still be supported by all of the ideas that preceded it. 

Step 10: Revise, Revise, Revise

Of course you should proofread your literary analysis essay before you turn it in. But you should also edit the content to make sure every piece of evidence and every explanation directly supports your thesis as effectively and efficiently as possible. 

Sometimes, this might mean actually adapting your thesis a bit to the rest of your essay. At other times, it means removing redundant examples or paraphrasing quotations. Make sure every sentence is valuable, and remove those that aren’t. 

Other Resources for Literary Analysis 

With these skills and suggestions, you’re well on your way to practicing and writing literary analysis. But if you don’t have a firm grasp on the concepts discussed above—such as literary devices or even the content of the text you’re analyzing—it will still feel difficult to produce insightful analysis. 

If you’d like to sharpen the tools in your literature toolbox, there are plenty of other resources to help you do so: 

• Check out our expansive library of Literary Devices . These could provide you with a deeper understanding of the basic devices discussed above or introduce you to new concepts sure to impress your professors ( anagnorisis , anyone?). 
• This Academic Citation Resource Guide ensures you properly cite any work you reference in your analytical essay. 
• Our English Homework Help Guide will point you to dozens of resources that can help you perform analysis, from critical reading strategies to poetry helpers. 
• This Grammar Education Resource Guide will direct you to plenty of resources to refine your grammar and writing (definitely important for getting an A+ on that paper). 

Of course, you should know the text inside and out before you begin writing your analysis. In order to develop a true understanding of the work, read through its corresponding SuperSummary study guide . Doing so will help you truly comprehend the plot, as well as provide some inspirational ideas for your analysis.

Textual Analysis: Definition, Types & 10 Examples

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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Table of Contents

Ai, ethics & human agency, collaboration, information literacy, writing process, textual analysis – how to engage in textual analysis.

• © 2023 by Jennifer Janechek - IBM Quantum

As a reader, a developing writer, and an informed student and citizen, you need to be able to locate, understand, and critically analyze others’ purposes in communicating information. Being able to identify and articulate the meaning of other writers’ arguments and theses enables you to engage in intelligent, meaningful, and critical knowledge exchanges. Ultimately, regardless of the discipline you choose to participate in,  textual analysis —the summary, contextualization, and interpretation of a writer’s effective or ineffective delivery of their perspective on a topic, statement of thesis, and development of an argument—will be an invaluable skill. Your ability to critically engage in knowledge exchanges—through the analysis of others’ communication—is integral to your success as a student and as a citizen.

Step 1: What Is The Thesis?

In order to learn how to better recognize a thesis in a written text, let’s consider the following argument:

So far, [Google+] does seem better than Facebook, though I’m still a rookie and don’t know how to do even some basic things.

It’s better in design terms, and also much better with its “circles” allowing you to target posts to various groups.

Example: following that high school reunion, the overwhelming majority of my Facebook friends list (which I’m barely rebuilding after my rejoin) are people from my own hometown. None of these people are going to care too much when my new book comes out from Edinburgh. Likewise, not too many of you would care to hear inside jokes about our old high school teachers, or whatever it is we banter about.

Another example: people I know only from exchanging a couple of professional emails with them ask to be Facebook friends. I’ve never met these people and have no idea what they’re really like, even if they seem nice enough on email. Do I really want to add them to my friends list on the same level as my closest friends, brothers, valued colleagues, etc.? Not yet. But then there’s the risk of offending people if you don’t add them. On Google+ you can just drop them in the “acquaintances” circle, and they’ll never know how they’re classified.

But they won’t be getting any highly treasured personal information there, which is exactly the restriction you probably want for someone you’ve never met before.

I also don’t like too many family members on my Facebook friends list, because frankly they don’t need to know everything I’m doing or chatting about with people. But on Google+ this problem will be easily manageable. (Harman)

The first sentence, “[Google+] does seem better than Facebook” (Harman), doesn’t communicate the writer’s position on the topic; it is merely an observation . A position, also called a “claim,” often includes the conjunction “because,” providing a reason why the writer’s observation is unique, meaningful, and critical.https://www.youtube.com/embed/rwSFfnlwtjY?rel=0&feature=youtu.beTherefore, if the writer’s sentence, “[Google+] does seem better than Facebook” (Harman), is simply an observation, then in order to identify the writer’s position, we must find the answer to “because, why?” One such answer can be found in the author’s rhetorical question/answer, “Do I really want to add them to my friends list on the same level as my closest friends, brothers, valued colleagues, etc.? Not yet” (Harman). The writer’s “because, why?” could be “because Google+ allows me to manage old, new, and potential friends and acquaintances using separate circles, so that I’m targeting posts to various, separate groups.” Therefore, the writer’s thesis—their position—could be something like, “Google+ is better than Facebook because its design enables me to manage my friends using separate circles, so that I’m targeting posts to various, separate groups instead of posting the same information for everyone I’ve added to my network.”

In addition to communicating a position on a particular topic, a writer’s thesis outlines what aspects of the topic they will address. Outlining intentions within a thesis is not only acceptable, but also one of a writer’s primary obligations, since the thesis relates their general argument. In a sense, you could think of the thesis as a responsibility to answer the question, “What will you/won’t you be claiming and why?”

To explain this further, let’s consider another example. If someone were to ask you what change you want to see in the world, you probably wouldn’t readily answer “world peace,” even though you (and many others) may want that. Why wouldn’t you answer that way? Because such an answer is far too broad and ambiguous to be logically argued. Although world peace may be your goal, for logic’s sake, you would be better off articulating your answer as “a peaceful solution to the violence currently occurring on the border of southern Texas and Mexico,” or something similarly specific. The distinction between the two answers should be clear: the first answer, “world peace,” is broad, ambiguous, and not a fully developed claim (there wouldn’t be many, if any, people who would disagree with this statement); the second answer is narrower, more specific, and a fully developed claim. It confines the argument to a particular example of violence, but still allows you to address what you want, “world peace,” on a smaller, more manageable, and more logical scale.

Since a writer’s thesis functions as an outline of what they will address in an argument, it is often organized in the same manner as the argument itself. Let’s return to the argument about Google+ for an example. If the author stated their position as suggested—“Google+ is better than Facebook because its design enables me to manage my friends using separate circles, so that I’m targeting posts to various, separate groups instead of posting the same information I’ve added to my network”—we would expect them to first address the similarities and differences between the designs of Google+ and Facebook, and then the reasons why they believe Google+ is a more effective way of sharing information. The organization of their thesis should reflect the overall order of their argument. Such a well-organized thesis builds the foundation for a cohesive and persuasive argument.

Textual Analysis: How is the Argument Structured?

“Textual analysis” is the term writers use to describe a reader’s written explanation of a text. The reader’s textual analysis ought to include a summary of the author’s topic, an analysis or explanation of how the author’s perspective relates to the ongoing conversation about that particular topic, an interpretation of the effectiveness of the author’s argument and thesis , and references to specific components of the text that support his or her analysis or explanation.

An effective argument generally consists of the following components:

• A thesis. Communicates the writer’s position on a particular topic.
• Acknowledgement of opposition. Explains existing objections to the writer’s position.
• Clearly defined premises outlining reasoning. Details the logic of the writer’s position.
• Evidence of validating premises. Proves the writer’s thorough research of the topic.
• A conclusion convincing the audience of the argument’s soundness/persuasiveness. Argues the writer’s position is relevant, logical, and thoroughly researched and communicated.

An effective argument also is specifically concerned with the components involved in researching, framing, and communicating evidence:

• The credibility and breadth of the writer’s research
• The techniques (like rhetorical appeals) used to communicate the evidence (see “The Rhetorical Appeals”)
• The relevance of the evidence as it reflects the concerns and interests of the author’s targeted audience

To identify and analyze a writer’s argument, you must critically read and understand the text in question. Focus and take notes as you read, highlighting what you believe are key words or important phrases. Once you are confident in your general understanding of the text, you’ll need to explain the author’s argument in a condensed summary. One way of accomplishing this is to ask yourself the following questions:

• What topic has the author written about? (Explain in as few words as possible.)
• What is the author’s point of view concerning their topic?
• What has the author written about the opposing point of view? (Where does it appear as though the author is “giving credit” to the opposition?)
• Does the author offer proof (either in reference to another published source or from personal experience) supporting their stance on the topic?
• As a reader, would you say that the argument is persuasive? Can you think of ways to strengthen the argument? Using which evidence or techniques?

Your articulation of the author’s argument will most likely derive from your answers to these questions. Let’s reconsider the argument about Google+ and answer the reflection questions listed above:

The author’s topic is two social networks—Google+ and Facebook.

The author is “for” the new social network Google+.

The author makes a loose allusion to the opposing point of view in the explanation, “I’m still a rookie and don’t know how to do even some basic things” (Harman). (The author alludes to his inexperience and, therefore, the potential for the opposing argument to have more merit.)

Yes, the author offers proof from personal experience, particularly through their first example: “following that high school reunion, the overwhelming majority of my Facebook friends list (which I’m barely rebuilding after my rejoin) are people from my hometown” (Harman). In their second example, they cite that “[o]n Google+ you can just drop [individuals] in the ‘acquaintances’ circle, and they’ll never even know how they’re classified” (Harman) in order to offer even more credible proof, based on the way Google+ operates instead of personal experience.

Yes, I would say that this argument is persuasive, although if I wanted to make it even stronger, I would include more detailed information about the opposing point of view. A balanced argument—one that fairly and thoroughly articulates both sides—is often more respected and better received because it proves to the audience that the writer has thoroughly researched the topic prior to making a judgment in favor of one perspective or another.

Works Cited

Harman, Graham. Object-Oriented Philosophy. WordPress, n.d. Web. 15 May 2012.

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Follow the assignment closely!  A textual analysis, like any other writing, has to have a specific audience and purpose, and you must carefully write it to serve that audience and fulfill that specific purpose.

�          In any analysis, the first sentence or the topic sentence mentions the title, author and main point of the article, and is written in grammatically correct English.

�          An analysis is written in your own words and takes the text apart bit by bit . It usually includes very few quotes but many references to the original text. It analyzes the text somewhat like a forensics lab analyzes evidence for clues: carefully, meticulously and in fine detail.  

�          In this particular type of reading analysis, you are not looking at all of the main ideas in a text, or the structure of the text.  Instead, y ou are given a question that has you explore just one or two main ideas in the text and you have to explain in detail what the text says about the assigned idea(s), focusing only on the content of the text.   Do not include your own response to the text.

�          An analysis is very specific, and should not include vague, poofy generalities.

�          The most common serious errors in this type of text analysis are * including irrelevant ideas from the text, * inserting your own opinions, or * omitting key relevant information from the text.

�            Any analysis is very closely focused on the text being analyzed, and is not the place to introduce your own original lines of thought, opinions, discussion or reaction on the ideas in question.  

�          When you quote anything from the original text, even an unusual word or a catchy phrase, you need to put whatever you quote in quotation marks (� �).  A good rule of thumb is that if the word or phrase you quote is not part of your own ordinary vocabulary (or the ordinary vocabulary of your intended audience), use quotation marks.  Quotes should be rare.

�          An analysis should end appropriately with a sense of closure (and not just stop because you run out of things to write!) and should finish up with a renewed emphasis on the ideas in question. However, DO NOT repeat what you wrote at the beginning of the analysis. 

�          It is not possible to analyze a text without reading the text through carefully first and understanding it.      

In an effective reading analysis paper:

Surface errors are few and do not distract the reader. 

OW ENGL 0310 rev 2/06

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How to Write an Analysis

Last Updated: April 3, 2024 Fact Checked

This article was co-authored by Christopher Taylor, PhD and by wikiHow staff writer, Megaera Lorenz, PhD . Christopher Taylor is an Adjunct Assistant Professor of English at Austin Community College in Texas. He received his PhD in English Literature and Medieval Studies from the University of Texas at Austin in 2014. There are 14 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 294,506 times.

An analysis is a piece of writing that looks at some aspect of a document in detail. To write a good analysis, you’ll need to ask yourself questions that focus on how and why the document works the way it does. You can start the process by gathering information about the subject of your analysis and defining the questions your analysis will answer. Once you’ve outlined your main arguments, look for specific evidence to support them. You can then work on putting your analysis together into a coherent piece of writing.

Gathering Information and Building Your Argument

• If your analysis is supposed to answer a specific question or focus on a particular aspect of the document you are analyzing.
• If there are any length or formatting requirements for the analysis.
• The citation style your instructor wants you to use.
• On what criteria your instructor will evaluate your analysis (e.g., organization, originality, good use of references and quotations, or correct spelling and grammar).

• The title of the document (if it has one).
• The name of the creator of the document. For example, depending on the type of document you’re working with, this could be the author, artist, director, performer, or photographer.
• The form and medium of the document (e.g., “Painting, oil on canvas”).
• When and where the document was created.
• The historical and cultural context of the work.

• Who you believe the intended audience is for the advertisement.
• What rhetorical choices the author made to persuade the audience of their main point.
• What product is being advertised.
• How the poster uses images to make the product look appealing.
• Whether there is any text in the poster, and, if so, how it works together with the images to reinforce the message of the ad.
• What the purpose of the ad is or what its main point is.

• For example, if you’re analyzing an advertisement poster, you might focus on the question: “How does this poster use colors to symbolize the problem that the product is intended to fix? Does it also use color to represent the beneficial results of using the product?”

• For example, you might write, “This poster uses the color red to symbolize the pain of a headache. The blue elements in the design represent the relief brought by the product.”
• You could develop the argument further by saying, “The colors used in the text reinforce the use of colors in the graphic elements of the poster, helping the viewer make a direct connection between the words and images.”

• For example, if you’re arguing that the advertisement poster uses red to represent pain, you might point out that the figure of the headache sufferer is red, while everyone around them is blue. Another piece of evidence might be the use of red lettering for the words “HEADACHE” and “PAIN” in the text of the poster.
• You could also draw on outside evidence to support your claims. For example, you might point out that in the country where the advertisement was produced, the color red is often symbolically associated with warnings or danger.

Tip: If you’re analyzing a text, make sure to properly cite any quotations that you use to support your arguments. Put any direct quotations in quotation marks (“”) and be sure to give location information, such as the page number where the quote appears. Additionally, follow the citation requirements for the style guide assigned by your instructor or one that's commonly used for the subject matter you're writing about.

Organizing and Drafting Your Analysis

• For example, “The poster ‘Say! What a relief,’ created in 1932 by designer Dorothy Plotzky, uses contrasting colors to symbolize the pain of a headache and the relief brought by Miss Burnham’s Pep-Em-Up Pills. The red elements denote pain, while blue ones indicate soothing relief.”

Tip: Your instructor might have specific directions about which information to include in your thesis statement (e.g., the title, author, and date of the document you are analyzing). If you’re not sure how to format your thesis statement or topic sentence, don’t hesitate to ask.

• a. Background
• ii. Analysis/Explanation
• iii. Example
• iv. Analysis/Explanation
• III. Conclusion

• For example, “In the late 1920s, Kansas City schoolteacher Ethel Burnham developed a patent headache medication that quickly achieved commercial success throughout the American Midwest. The popularity of the medicine was largely due to a series of simple but eye-catching advertising posters that were created over the next decade. The poster ‘Say! What a relief,’ created in 1932 by designer Dorothy Plotzky, uses contrasting colors to symbolize the pain of a headache and the relief brought by Miss Burnham’s Pep-Em-Up Pills.”

• Make sure to include clear transitions between each argument and each paragraph. Use transitional words and phrases, such as “Furthermore,” “Additionally,” “For example,” “Likewise,” or “In contrast . . .”
• The best way to organize your arguments will vary based on the individual topic and the specific points you are trying to make. For example, in your analysis of the poster, you might start with arguments about the red visual elements and then move on to a discussion about how the red text fits in.

• For example, you might end your essay with a few sentences about how other advertisements at the time might have been influenced by Dorothy Plotzky’s use of colors.

• For example, in your discussion of the advertisement, avoid stating that you think the art is “beautiful” or that the advertisement is “boring.” Instead, focus on what the poster was supposed to accomplish and how the designer attempted to achieve those goals.

Polishing Your Analysis

• For example, if your essay currently skips around between discussions of the red and blue elements of the poster, consider reorganizing it so that you discuss all the red elements first, then focus on the blue ones.

• For example, you might look for places where you could provide additional examples to support one of your major arguments.

• For example, if you included a paragraph about Dorothy Plotzky’s previous work as a children’s book illustrator, you may want to cut it if it doesn’t somehow relate to her use of color in advertising.
• Cutting material out of your analysis may be difficult, especially if you put a lot of thought into each sentence or found the additional material really interesting. Your analysis will be stronger if you keep it concise and to the point, however.

• You may find it helpful to have someone else go over your essay and look for any mistakes you might have missed.

Tip: When you’re reading silently, it’s easy to miss typos and other small errors because your brain corrects them automatically. Reading your work out loud can make problems easier to spot.

Sample Analysis Outline and Conclusion

Expert Q&A

You Might Also Like

• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-make-sure-i-understand-an-assignment-.html
• ↑ https://www.bucks.edu/media/bcccmedialibrary/pdf/HOWTOWRITEALITERARYANALYSISESSAY_10.15.07_001.pdf
• ↑ https://owl.purdue.edu/owl/general_writing/visual_rhetoric/analyzing_visual_documents/elements_of_analysis.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-can-i-create-stronger-analysis-.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-decide-what-i-should-argue-.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-effectively-integrate-textual-evidence-.html
• ↑ https://writingcenter.uagc.edu/writing-a-thesis
• ↑ https://owl.purdue.edu/owl/general_writing/visual_rhetoric/analyzing_visual_documents/organizing_your_analysis.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-write-an-intro--conclusion----body-paragraph.html
• ↑ http://utminers.utep.edu/omwilliamson/engl0310/Textanalysis.htm
• ↑ https://owl.purdue.edu/owl/graduate_writing/graduate_writing_topics/graduate_writing_organization_structure_new.html
• ↑ https://owl.purdue.edu/owl/general_writing/mechanics/sentence_clarity.html
• ↑ https://writingcenter.unc.edu/tips-and-tools/conciseness-handout/
• ↑ https://writingcenter.unc.edu/tips-and-tools/editing-and-proofreading/

About This Article

If you need to write an analysis, first look closely at your assignment to make sure you understand the requirements. Then, gather background information about the document you’ll be analyzing and do a close read so that you’re thoroughly familiar with the subject matter. If it’s not already specified in your assignment, come up with one or more specific question’s you’d like your analysis to answer, then outline your main arguments. Finally, gather evidence and examples to support your arguments. Read on to learn how to organize, draft, and polish your analysis! Did this summary help you? Yes No

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Analyzing a Text

Written texts.

When you analyze an essay or article, consider these questions:

• What is the thesis or central idea of the text?
• Who is the intended audience?
• What questions does the author address?
• How does the author structure the text?
• What are the key parts of the text?
• How do the key parts of the text interrelate?
• How do the key parts of the text relate to the thesis?
• What does the author do to generate interest in the argument?
• How does the author convince the readers of their argument’s merit?
• What evidence is provided in support of the thesis?
• Is the evidence in the text convincing?
• Has the author anticipated opposing views and countered them?
• Is the author’s reasoning sound?

Visual Texts

When you analyze a piece of visual work, consider these questions:

• What confuses, surprises, or interests you about the image?
• In what medium is the visual?
• Where is the visual from?
• Who created the visual?
• For what purpose was the visual created?
• Identify any clues that suggest the visual’s intended audience.
• How does this image appeal to that audience?
• In the case of advertisements, what product is the visual selling?
• In the case of advertisements, is the visual selling an additional message or idea?
• If words are included in the visual, how do they contribute to the meaning?
• Identify design elements – colors, shapes, perspective, and background – and speculate how they help to convey the visual’s meaning or purpose.

About Writing: A Guide Copyright © 2015 by Robin Jeffrey is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Home » Critical Analysis – Types, Examples and Writing Guide

Critical Analysis – Types, Examples and Writing Guide

Table of Contents

Critical Analysis

Definition:

Critical analysis is a process of examining a piece of work or an idea in a systematic, objective, and analytical way. It involves breaking down complex ideas, concepts, or arguments into smaller, more manageable parts to understand them better.

Types of Critical Analysis

Types of Critical Analysis are as follows:

Literary Analysis

This type of analysis focuses on analyzing and interpreting works of literature , such as novels, poetry, plays, etc. The analysis involves examining the literary devices used in the work, such as symbolism, imagery, and metaphor, and how they contribute to the overall meaning of the work.

Film Analysis

This type of analysis involves examining and interpreting films, including their themes, cinematography, editing, and sound. Film analysis can also include evaluating the director’s style and how it contributes to the overall message of the film.

Art Analysis

This type of analysis involves examining and interpreting works of art , such as paintings, sculptures, and installations. The analysis involves examining the elements of the artwork, such as color, composition, and technique, and how they contribute to the overall meaning of the work.

Cultural Analysis

This type of analysis involves examining and interpreting cultural artifacts , such as advertisements, popular music, and social media posts. The analysis involves examining the cultural context of the artifact and how it reflects and shapes cultural values, beliefs, and norms.

Historical Analysis

This type of analysis involves examining and interpreting historical documents , such as diaries, letters, and government records. The analysis involves examining the historical context of the document and how it reflects the social, political, and cultural attitudes of the time.

Philosophical Analysis

This type of analysis involves examining and interpreting philosophical texts and ideas, such as the works of philosophers and their arguments. The analysis involves evaluating the logical consistency of the arguments and assessing the validity and soundness of the conclusions.

Scientific Analysis

This type of analysis involves examining and interpreting scientific research studies and their findings. The analysis involves evaluating the methods used in the study, the data collected, and the conclusions drawn, and assessing their reliability and validity.

Critical Discourse Analysis

This type of analysis involves examining and interpreting language use in social and political contexts. The analysis involves evaluating the power dynamics and social relationships conveyed through language use and how they shape discourse and social reality.

Comparative Analysis

This type of analysis involves examining and interpreting multiple texts or works of art and comparing them to each other. The analysis involves evaluating the similarities and differences between the texts and how they contribute to understanding the themes and meanings conveyed.

Critical Analysis Format

Critical Analysis Format is as follows:

I. Introduction

• Provide a brief overview of the text, object, or event being analyzed
• Explain the purpose of the analysis and its significance
• Provide background information on the context and relevant historical or cultural factors

II. Description

• Provide a detailed description of the text, object, or event being analyzed
• Identify key themes, ideas, and arguments presented
• Describe the author or creator’s style, tone, and use of language or visual elements

III. Analysis

• Analyze the text, object, or event using critical thinking skills
• Identify the main strengths and weaknesses of the argument or presentation
• Evaluate the reliability and validity of the evidence presented
• Assess any assumptions or biases that may be present in the text, object, or event
• Consider the implications of the argument or presentation for different audiences and contexts

IV. Evaluation

• Provide an overall evaluation of the text, object, or event based on the analysis
• Assess the effectiveness of the argument or presentation in achieving its intended purpose
• Identify any limitations or gaps in the argument or presentation
• Consider any alternative viewpoints or interpretations that could be presented
• Summarize the main points of the analysis and evaluation
• Reiterate the significance of the text, object, or event and its relevance to broader issues or debates
• Provide any recommendations for further research or future developments in the field.

VI. Example

• Provide an example or two to support your analysis and evaluation
• Use quotes or specific details from the text, object, or event to support your claims
• Analyze the example(s) using critical thinking skills and explain how they relate to your overall argument

VII. Conclusion

• Reiterate your thesis statement and summarize your main points
• Provide a final evaluation of the text, object, or event based on your analysis
• Offer recommendations for future research or further developments in the field
• End with a thought-provoking statement or question that encourages the reader to think more deeply about the topic

How to Write Critical Analysis

Writing a critical analysis involves evaluating and interpreting a text, such as a book, article, or film, and expressing your opinion about its quality and significance. Here are some steps you can follow to write a critical analysis:

• Read and re-read the text: Before you begin writing, make sure you have a good understanding of the text. Read it several times and take notes on the key points, themes, and arguments.
• Identify the author’s purpose and audience: Consider why the author wrote the text and who the intended audience is. This can help you evaluate whether the author achieved their goals and whether the text is effective in reaching its audience.
• Analyze the structure and style: Look at the organization of the text and the author’s writing style. Consider how these elements contribute to the overall meaning of the text.
• Evaluate the content : Analyze the author’s arguments, evidence, and conclusions. Consider whether they are logical, convincing, and supported by the evidence presented in the text.
• Consider the context: Think about the historical, cultural, and social context in which the text was written. This can help you understand the author’s perspective and the significance of the text.
• Develop your thesis statement : Based on your analysis, develop a clear and concise thesis statement that summarizes your overall evaluation of the text.
• Support your thesis: Use evidence from the text to support your thesis statement. This can include direct quotes, paraphrases, and examples from the text.
• Write the introduction, body, and conclusion : Organize your analysis into an introduction that provides context and presents your thesis, a body that presents your evidence and analysis, and a conclusion that summarizes your main points and restates your thesis.
• Revise and edit: After you have written your analysis, revise and edit it to ensure that your writing is clear, concise, and well-organized. Check for spelling and grammar errors, and make sure that your analysis is logically sound and supported by evidence.

When to Write Critical Analysis

You may want to write a critical analysis in the following situations:

• Academic Assignments: If you are a student, you may be assigned to write a critical analysis as a part of your coursework. This could include analyzing a piece of literature, a historical event, or a scientific paper.
• Journalism and Media: As a journalist or media person, you may need to write a critical analysis of current events, political speeches, or media coverage.
• Personal Interest: If you are interested in a particular topic, you may want to write a critical analysis to gain a deeper understanding of it. For example, you may want to analyze the themes and motifs in a novel or film that you enjoyed.
• Professional Development : Professionals such as writers, scholars, and researchers often write critical analyses to gain insights into their field of study or work.

Critical Analysis Example

An Example of Critical Analysis Could be as follow:

Research Topic:

The Impact of Online Learning on Student Performance

Introduction:

The introduction of the research topic is clear and provides an overview of the issue. However, it could benefit from providing more background information on the prevalence of online learning and its potential impact on student performance.

Literature Review:

The literature review is comprehensive and well-structured. It covers a broad range of studies that have examined the relationship between online learning and student performance. However, it could benefit from including more recent studies and providing a more critical analysis of the existing literature.

Research Methods:

The research methods are clearly described and appropriate for the research question. The study uses a quasi-experimental design to compare the performance of students who took an online course with those who took the same course in a traditional classroom setting. However, the study may benefit from using a randomized controlled trial design to reduce potential confounding factors.

The results are presented in a clear and concise manner. The study finds that students who took the online course performed similarly to those who took the traditional course. However, the study only measures performance on one course and may not be generalizable to other courses or contexts.

Discussion :

The discussion section provides a thorough analysis of the study’s findings. The authors acknowledge the limitations of the study and provide suggestions for future research. However, they could benefit from discussing potential mechanisms underlying the relationship between online learning and student performance.

Conclusion :

The conclusion summarizes the main findings of the study and provides some implications for future research and practice. However, it could benefit from providing more specific recommendations for implementing online learning programs in educational settings.

Purpose of Critical Analysis

There are several purposes of critical analysis, including:

• To identify and evaluate arguments : Critical analysis helps to identify the main arguments in a piece of writing or speech and evaluate their strengths and weaknesses. This enables the reader to form their own opinion and make informed decisions.
• To assess evidence : Critical analysis involves examining the evidence presented in a text or speech and evaluating its quality and relevance to the argument. This helps to determine the credibility of the claims being made.
• To recognize biases and assumptions : Critical analysis helps to identify any biases or assumptions that may be present in the argument, and evaluate how these affect the credibility of the argument.
• To develop critical thinking skills: Critical analysis helps to develop the ability to think critically, evaluate information objectively, and make reasoned judgments based on evidence.
• To improve communication skills: Critical analysis involves carefully reading and listening to information, evaluating it, and expressing one’s own opinion in a clear and concise manner. This helps to improve communication skills and the ability to express ideas effectively.

Importance of Critical Analysis

Here are some specific reasons why critical analysis is important:

• Helps to identify biases: Critical analysis helps individuals to recognize their own biases and assumptions, as well as the biases of others. By being aware of biases, individuals can better evaluate the credibility and reliability of information.
• Enhances problem-solving skills : Critical analysis encourages individuals to question assumptions and consider multiple perspectives, which can lead to creative problem-solving and innovation.
• Promotes better decision-making: By carefully evaluating evidence and arguments, critical analysis can help individuals make more informed and effective decisions.
• Facilitates understanding: Critical analysis helps individuals to understand complex issues and ideas by breaking them down into smaller parts and evaluating them separately.
• Fosters intellectual growth : Engaging in critical analysis challenges individuals to think deeply and critically, which can lead to intellectual growth and development.

Advantages of Critical Analysis

Some advantages of critical analysis include:

• Improved decision-making: Critical analysis helps individuals make informed decisions by evaluating all available information and considering various perspectives.
• Enhanced problem-solving skills : Critical analysis requires individuals to identify and analyze the root cause of a problem, which can help develop effective solutions.
• Increased creativity : Critical analysis encourages individuals to think outside the box and consider alternative solutions to problems, which can lead to more creative and innovative ideas.
• Improved communication : Critical analysis helps individuals communicate their ideas and opinions more effectively by providing logical and coherent arguments.
• Reduced bias: Critical analysis requires individuals to evaluate information objectively, which can help reduce personal biases and subjective opinions.
• Better understanding of complex issues : Critical analysis helps individuals to understand complex issues by breaking them down into smaller parts, examining each part and understanding how they fit together.
• Greater self-awareness: Critical analysis helps individuals to recognize their own biases, assumptions, and limitations, which can lead to personal growth and development.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Core Detail: Instructional Modes

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Core Detail: Weekly Schedule

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Portfolios?

Portfolio Overview - Thomas

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Purpose/Context

Topic and Position

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Proof/Evidence

Organization

First two paragraphs: The authors critique other people's readings of the novel.

Paragraph 3: They explains that their own reading is more accurate because it accounts for the details others leave out.

Drawing Conclusions

• If you were trying to write for this publication, what are the most important or notable conventions that you would have to follow? In other words, what strategies would you use in order to prove yourself to be a successful writer in this field?

How to Write a Literary Analysis Essay Step by Step

So, you’ve been assigned a literary analysis essay. Don’t panic! It’s not a big deal, for sure. Here’s a simple step-by-step guide to help you ace it:

1. Understand the Prompt

Recognizing that identifying the main topic and simply reading through the given instructions is the essential first step to writing an outstanding essay. You should first carefully read the given sentences which include verbs like “analyze,” “discuss,” or “explore.”

It points out that your professor is specifically interested in a particular element of the text, maybe a theme, character or some kind of literary device. Thus, this approach will spare any misinterpretation through highlighting the most critical points of the job and how it is to be executed.

If you struggle with understanding the prompt, ask for help today at the quick essay writing service FastEssay . You may ask academic writers to explain to you how to write such papers quickly and easily.

2. Select the Literary Work

Everything begins with the right story, absolutely! Pick a work that is not just a part of your arsenal of knowledge but also something that you like. The second essay is a genre(e.g., novel, short story, poetry or drama) that can be focused on. The fact that you will apply the ingredients: it will not only increase the interest in the students but also create curiosity and will turn this process into a more interesting and challenging one.

3. Read and Re-read

Decision having been made, you must plunge yourself into the text. Close your eyes, and imagine the reality of the novel, where you are one of the characters yourself, or the place they are in, or the events that have happened, or the language they use. Consider doing things slowly instead of in fast mode.Study the text carefully. The probability is, you are going to get the deeper meaning and the linkages that you might have missed in the first reading when you read the text several times.

4. Identify the Thesis

Your thesis is the heart of your literary analysis essay—it is the core argument you will advance based on the text. Spend some time to come up with a thesis statement, after which you can begin your brainstorming. It should be relevant, concise, and specific either by defining the purpose of the whole analysis or stating the central idea to be examined. Your thesis will be the guiding principle of the essay and it should be obvious to the reader from the time of the first sentence.

5. Gather Evidence

Having the thesis sub-part done, you will now need to present the text evidence from which you will be able to support your argument. Seek out quotations, sections, or instances that validate the stated argument. These instances can be a symbol, an image, a speech by a character, or plot developments. Evidence the things that support your argument and are factual for your analysis in order to reduce the impact of the interfering factors.

6. Analyze the Text

Hence, now you will have to make use of your evidence to do the analysis. Now, separate the text into smaller parts and analyze how literary devices used in the text make it more meaningful. Take into account why the craftsman takes certain decisions and what effect these decisions have on the audience. Examine how devices like symbolism, imagery, irony, and foreshadowing strengthen the message and main ideas of the text.

7. Outline Your Essay

To start your work, you might want to outline your thinking and evidence to be able to organize them. Structure your literary analysis essay by dividing it into sections: Introduction, body paragraphs, and conclusion. Every paragraph will be devoted to a single sub-topic of your analysis and should begin with a clear sentence that indicates its purpose followed by appropriate evidence to back it up.

8. Write the Introduction

The opening part of the literary analysis essay is a place where you demonstrate your approach to your writing and where the reader should feel interested from the beginning. The best way to start is with a hook—an interesting one liner, a question, or an incident—that will make the reader want to read on and at the same time establish the importance of your analysis. Starting off, give the readers some information about the text, its author and the point your essay will drive home, which should be a clear statement of your thesis.

9. Develop Body Paragraphs

Body paragraphs is a place where you analyze in depth your viewpoint using supporting evidence. Ensure that each of your paragraphs starts with a topic sentence which identifies the main point or argument that you are going to explain in that paragraph. Next, you are required to provide support from a text that is giving a basis to a claim, being sure that you have analyzed each sentence and explained its meaning in relation to the thesis. Include examples, quotes, and citations to bolster your argument and have the reader accept the deconstruction you made.

10. Transition Smoothly

As you shift to the next paragraph in your literary analysis essay, be sure that the logical flow is not disrupted. Make use of transition words and phrases like “nevertheless”, “additionally”, “furthermore” and “beyond this” to link up your ideas together in a coherent manner. This assists the reader to follow your thought and make the logical flow of your thinking more obvious.

11. Write the Conclusion

A conclusion is like a period to an essay where you re-echo your points and state your thesis using different words. Try not to make a conclusion that is different from the one you have made or that is not related to the topic of analysis—the conclusion you make should be aimed at leaving the reader with a lasting impression. Finish by making a thought-provoking remark or an invitation to action that would leave a mark on your readers’ minds when they are thinking about the text.

12. Revise and Edit

The first draft is over, so sit down and respire for a while to reevaluate what you’ve written. Be careful about grammar, punctuation, and sentence architecture and check if your piece is not confusing and full of grammatical errors. First, see whether possible weakening or clarification of your analysis would be needed and edit the text accordingly.

13. Seek Feedback

So, don’t be afraid to ask your peers, mates or instructor for assessment when needed. Seeing the world through a different lens brings a lot of fresh perspectives that you haven’t thought of yet. Balance their feedback in your essay to adjust and revise the text with care in order to make it smarter.

So, don’t forget that in the beginning it may seem a bit difficult but with time and practice you will be a real pro in writing a literary analysis essay . The more you read literature and refine your analytical skills, you will recognize that the dissecting and interpreting of the text will get easier. Thus, my message to you is: do not be afraid to fully engage yourself in literary criticism and discover what lies at the core of your favorite novels. Happy writing!

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7 Simple Techniques to Analyze Your Text for Better Writing

Analyzing texts is a vital skill for improving writing. By examining different texts, you can learn a lot about structure, style, and content. This knowledge is key to enhancing your own writing. Understanding how authors construct their works gives you tools to develop your style. It’s like uncovering a roadmap to effective writing. By studying various texts, you gain insights that can transform your writing, making it more compelling and polished.

Understanding Text Structure

Recognizing text structures is crucial in writing. Each structure, be it narrative, expository, or persuasive, serves a different purpose. Understanding these can guide your writing approach. For example, a narrative structure focuses on storytelling, while expository aims to inform and explain.

One way to grasp these structures is through examples. Consider using a paper writing service like EssayPro to see samples. These services often provide well-crafted examples that illustrate different structures effectively.

When analyzing texts, look for clues. Narratives often use descriptive language and personal anecdotes, expository texts present facts and explanations, and persuasive writings argue a point with supporting evidence. Identifying these elements helps in applying them to your writing.

Analyzing Writing Style

Analyzing an author’s writing style involves focusing on elements like tone, word choice, and sentence structure. The tone of a text can range from formal to conversational, serious to humorous. Pay attention to how the tone influences the reader’s engagement.

Word choice is another critical element. Notice whether the language is simple or complex, abstract or concrete. This can reveal a lot about the author’s intent and audience.

Finally, examine the sentence structure. Short, punchy sentences can create a fast-paced narrative, while longer, complex sentences might be used for detailed descriptions or arguments. Understanding these elements can help you develop a versatile and effective writing style in your writing.

Theme and Content Analysis

Identifying themes and main ideas is key in text analysis. Themes are the underlying messages or central ideas of a text. To find them, look for recurring topics or concepts. Ask yourself what the author is trying to convey about these topics.

For complex ideas, break them down into smaller parts. Analyze each part separately and consider how they connect. Look for patterns or contrasts in the text. This helps in understanding the broader theme or message.

Summarizing each paragraph can also be helpful. It allows you to see how ideas develop and interact throughout the text, leading to a clearer understanding of the main theme.

Character and Plot Analysis (For Fiction)

In analyzing characters in fiction, focus on their development, motivations, and interactions. Look at how they evolve and respond to challenges. This understanding can enrich your character creation.

Plot analysis involves understanding the sequence of events and their impact on the narrative. Identify key plot points and how they drive the story forward. Notice the conflict and resolution patterns.

Applying these insights to your writing can enhance character depth and plot structure. Use character analysis to create believable, dynamic characters. For plot, borrow structural elements like rising action, climax, and resolution to craft compelling narratives. Understanding these elements in existing texts can significantly improve your storytelling skills.

Use of Literary Devices

Recognizing literary devices like metaphors , similes, and symbolism requires attention to detail. Metaphors and similes create vivid imagery by comparing things, often enhancing a reader’s understanding and experience. Symbolism, on the other hand, involves using objects or actions to represent deeper meanings or concepts.

These devices add depth and layers to writing, allowing readers to engage with the text on a more meaningful level. To incorporate them effectively in your own writing, practice identifying them in texts you read. Then, experiment with using them to add richness and complexity to your narratives or descriptions, enhancing the overall impact of your writing.

Comparing Texts

Comparing and contrasting texts is like using the best coursework writing service – it’s about finding quality insights from different sources. Start by choosing texts with similarities in theme or style, then identify their differences. Look at aspects like tone, structure, and literary devices. Note how each text approaches these elements uniquely.

This practice broadens your perspective, exposing you to diverse writing styles and ideas. By analyzing these differences and similarities, you can develop a more nuanced understanding of writing techniques, which can then be applied to enhance your own writing style and content.

Applying Analysis to Writing

Applying insights from text analysis to your writing can significantly improve your skills. Use the structures you’ve identified to organize your content effectively. If a certain tone resonates with you, try incorporating a similar style in your writing. Experiment with literary devices you’ve analyzed to add depth and interest to your work.

Remember, experimentation is key. Don’t be afraid to try different techniques and styles. This process helps you find your unique voice and enhances your writing versatility. Keep practicing and revisiting the texts you admire to continually refine and evolve your writing style.

Text analysis is an invaluable tool for writers, offering insights into various writing styles, structures, and techniques. By regularly analyzing texts , you can enhance your understanding of effective writing and apply these learnings to your work. Embrace this practice as part of your writing routine. It can sharpen your skills, broaden your perspectives, and ultimately lead to more refined and compelling writing. Keep exploring and learning from different texts to continually grow as a writer.

Analyse Englisch

So einfach ist eine Analyse in Englisch! Hier erfährst du, wie eine Analyse aufgebaut ist und was du beim Schreiben beachten solltest. In unserem Video erklären wir dir alles Wichtige zur Analyse in Englisch in wenigen Minuten.

Wie schreibt man eine Analyse auf Englisch?

Eine analyse in englisch schreiben – aufbau, tipps und tricks, connectives.

Welche Art von Analyse in Englisch du schreibst, hängt von deinem Originaltext ab. Dabei unterscheidest du zwischen fictional und  non-fictional texts .

Analysen von fictional texts , die eine ausgedachte Geschichte erzählen, beschäftigen sich zum Beispiel mit Dramen, Gedichten und Erzählungen. Analysen von non-fictional texts , also Sachtextanalysen , betrachten Zeitungsartikel, politische Reden oder wissenschaftliche Texte.

Im Grunde ist eine Analyse in Englisch aber immer dasselbe: Eine Aufsatzform , in der du einen Text auf verschiedene Aspekte wie Sprache und Struktur untersuchst. Aber auch Stimmung, Erzählperspektive und Autorintention können eine wichtige Rolle spielen. 

Im Großen und Ganzen ist eine Analyse in Englisch immer gleich aufgebaut. Sie besteht aus

• Einleitung (introduction)
• Hauptteil (body) 2.1 Inhaltsangabe (summary) 2.2 Deutungshypothese 2.3 Analyse
• Schluss (conclusion)

Im Folgenden erfährst du, wie diese Strukturteile generell aufgebaut sind und welche Besonderheiten es bei manchen Textarten gibt. Häufig reicht es aber, wenn du die Grundlagen der Analyse in Englisch kennst und sie an die jeweilige Textform und an die Aufgabenstellung anpasst.

Analyse in Englisch – Einleitung

Du beginnst deine Analyse in Englisch immer mit einem Einleitungssatz, der folgende allgemeine Informationen zu dem Text benennt:

• Textsorte (text type)
• Titel (title)
• Autor (author)
• Erscheinungsdatum (date)
• und Thema (main topic) des Analysetextes. 

Du kannst dir dafür folgendes Muster merken: The [ text type ] “[ title ],” which was written by [ author ] and published in [ date ], is about/deals with/addresses/is concerned with [ main topic ]. 

Der Einleitungssatz für einen Roman (novel) könnte zum Beispiel so lauten: The novel “ White Teeth ,“   which was written by Zadie Smith and published in 2000 , deals with different generations of immigrants in England .

Analyse in Englisch – Besonderheiten bei der Einleitung

Bei manchen Textsorten, wie zum Beispiel einem Zeitungsartikel, einer politischen Rede oder einem Drama, gibt es einige  Besonderheiten , die du in deinem Einleitungssatz beachten solltest.

Bei einem Zeitungsartikel nennst du zusätzlich immer den Namen der Zeitung , die den Artikel veröffentlicht hat. Das sieht dann so aus:

Beispiel: In her article “Multiculturalism – a Recipe for Racism,” which was published in The Guardian on 29 May 2001, Minette Marin addresses the benefits and drawbacks of multiculturalism in a society.

Bei einer politischen Rede gibst du statt einem Autor den Redner (speaker) an. Zudem nennst du zusätzlich den Ort (place) , den Anlass (occasion) und das Publikum der Rede (audience) . 

Beispiel:   Barack Obama’s “Victory Speech,” given on election night on 6 November 2012 in Washington, D.C. , contains one important message for the American people : They need to move forward!

Für die Analyse einer Rede in Englisch schaust du dir am besten noch unser Video zur speech analysis an.

Bei einem Drama gibst du nicht nur den Titel an, sondern auch den Akt (act I, II, III) und die Szene (scene 1, 2, 3) , auf die du dich in deiner Analyse beziehst. 

Beispiel: In this excerpt from act I , scene 5 of William Shakespeare’s “ Romeo and Juliet ,” which was published in 1597, Romeo Montague and Juliet Capulet meet for the first time.

Tipp:   N i cht immer sind dir alle Informationen bekannt. Versuche einfach, deinen Einleitungssatz so vollständig wie möglich zu machen.

Analyse in Englisch – Hauptteil

Der Hauptteil deiner Analyse besteht aus drei Teilen: der Summary , der Deutungshypothese und der Vorstellung deiner Analyseergebnisse . 

Nach der Einleitung fasst du zunächst den Inhalt des Textes in einer kurzen Summary zusammen. Auch hier gibt es abhängig von der Textart ein paar Besonderheiten zu beachten: 

• Bei einem Gedicht gibst du den Inhalt chronologisch von Strophe zu Strophe (stanza to stanza) wieder: In the first stanza …  , The second stanza is about … .
• Wenn dir ein Auszug aus einem Drama oder einem Roman vorliegt, ordnest du den Inhalt sinnvoll in den Gesamtzusammenhang des Werkes ein. Beschreibe dafür wichtige Figuren und relevante vorangegangene Geschehnisse. 

Wichtig: Wenn du bereits in der ersten Aufgabe eine Inhaltsangabe schreiben solltest, wiederholst du diese in deiner Analyse nicht.  Du beginnst stattdessen direkt mit deiner Deutungshypothese. 

Deutungshypothese

In deiner Deutungshypothese stellst du eine Vermutung darüber an, welche Absicht der Autor oder Redner mit seinem Text verfolgt. Dafür erklärst du kurz, was deiner Meinung nach Ziel und Effekt des Textes sind. 

Beispiel: The structure in form of a sonnet is supposed to guarantee the audience’s better understanding of the atmosphere created in this poem. 

Tipp: Deine Hypothese muss nicht bewiesen oder korrekt sein. Sie gibt dir und dem Leser nur den roten Faden deiner Untersuchung vor. Im Schluss überprüfst du deine Hypothese und passt sie gegebenenfalls an deine Analyseergebnisse an.

Darstellung der Analyseergebnisse

Den größten Teil deiner Analyse in Englisch macht die Darstellung deiner Analyseergebnisse aus. 

Bei Sachtextanalysen in Englisch zu non-fictional texts werden häufig die sprachliche Gestaltung (style) , der Aufbau (structure) , die Zeitform (tense) und die sprachlichen Mittel ( stylistic devices )   untersucht. 

In fictional texts stehen hingegen häufig die sprachliche Gestaltung (style) , die Erzählperspektive (narrative perspective) , die Handlungsstruktur (plot) , die Figurenkonstellation (character constellation) und das Erzählzeit  (narrative tense) im Vordergrund.

Wichtig: Achte dafür auf die Aufgabenstellung!  Darin wird dir in der Regel ein  Schwerpunkt für deine Analyse vorgegeben.

Eine Aufgabenstellung könnte zum Beispiel  so lauten: Analyse the language used in this excerpt. What is its effect?

In diesem Fall beschäftigst du dich mit der sprachlichen Form des Textes, seinen Stilmitteln und deren  Wirkung . Eine Analyse zu der Figurenkonstellation und der Handlungsstruktur ist hier hingegen nicht gefragt .

Analyse in Englisch – Schluss

Im Schlussteil deiner Analyse in Englisch fasst du die wesentlichen Ergebnisse deiner Untersuchung kurz zusammen. Dazu beziehst du dich noch einmal auf deine Deutungshypothese vom Anfang. Mit Blick auf deine Analyseergebnisse erklärst du, ob deine Hypothese bestätigt oder widerlegt wurde. Du kannst sie außerdem an deine Untersuchungserkenntnisse anpassen. 

Auch hier gibt es einige Besonderheiten je nach Textart zu beachten: 

• Wenn du eine Szene aus einem Drama oder einem Roman analysiert hast, beschreibst du hier zusätzlich die Bedeutung der Szene für den Gesamtzusammenhang. Überlege dafür zum Beispiel, ob die Szene einen Höhe- oder Wendepunkt in dem Werk darstellt.
• Hast du eine Rede oder einen Artikel untersucht, gibst du im Schluss auch die Absicht – die Intention – des Redners bzw. Autors wieder. 

Ein Schlussteil zur Analyse einer Rede könnte zum Beispiel so aussehen: 

Beispiel:   In summary, the speaker uses rhetorical devices, such as direct address, inclusive pronouns, and climaxes, to build confidence and solidarity among his listeners .

Jetzt kennst du dich mit den grundlegenden Elementen einer Analyse in Englisch aus. Diese allgemeinen Tipps und Tricks helfen dir zusätzlich beim Schreiben: 

• Vermeide Unterüberschriften! Einzelne Argumente und Analyseergebnisse machst du nur durch Absätze deutlich. 
• Verwende topic sentences ! Das heißt, du leitest jeden Absatz durch einen Satz ein, der die Hauptaussage dieses Absatzes deutlich macht.
• Achte immer auf die Aufgabenstellung ! In der Regel wird dir genau vorgegeben, mit welchen Aspekten du dich in deiner Analyse beschäftigen sollst. 
• Verzichte auf Wertungen ! Bleibe sachlich und beschäftige dich nur mit der Meinung, den Aussagen und der Absicht des Autors/Redners. 
• Wie immer: Vergiss nicht, Korrektur zu lesen! Achte besonders darauf, dass du richtig zitierst und die Regeln zur Kommasetzung in Englisch beachtest.

Du brauchst noch mehr Anleitung dazu, wie du deine Analyse verständlich gliederst? Dann schau dir unser Video zu den connectives an!  

Beliebte Inhalte aus dem Bereich Textarten Englisch

• Cartoon analysis Dauer: 03:47
• Cartoon analysis example Dauer: 03:41
• Speech analysis Dauer: 04:29

Weitere Inhalte: Textarten Englisch

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What is Text Analysis? A Beginner’s Guide

• How Does It work?
• Use Cases & Applications
• Online Tools

If you receive huge amounts of unstructured data in the form of text (emails, social media conversations, chats), you’re probably aware of the challenges that come with analyzing this data.

Manually processing and organizing text data takes time, it’s tedious, inaccurate, and it can be expensive if you need to hire extra staff to sort through text.

Automate text analysis with a no-code tool

In this guide, learn more about what text analysis is, how to perform text analysis using AI tools, and why it’s more important than ever to automatically analyze your text in real time.

• Text Analysis Basics
• Methods & Techniques

How Does Text Analysis Work?

How to analyze text data.

• Use Cases and Applications
• Tools and Resources

What Is Text Analysis?

Text analysis (TA) is a machine learning technique used to automatically extract valuable insights from unstructured text data. Companies use text analysis tools to quickly digest online data and documents, and transform them into actionable insights.

You can us text analysis to extract specific information, like keywords, names, or company information from thousands of emails, or categorize survey responses by sentiment and topic.

The Text Analysis vs. Text Mining vs. Text Analytics

Firstly, let's dispel the myth that text mining and text analysis are two different processes. The terms are often used interchangeably to explain the same process of obtaining data through statistical pattern learning. To avoid any confusion here, let's stick to text analysis.

So, text analytics vs. text analysis : what's the difference?

Text analysis delivers qualitative results and text analytics delivers quantitative results. If a machine performs text analysis, it identifies important information within the text itself, but if it performs text analytics, it reveals patterns across thousands of texts, resulting in graphs, reports, tables etc.

Let's say a customer support manager wants to know how many support tickets were solved by individual team members. In this instance, they'd use text analytics to create a graph that visualizes individual ticket resolution rates.

However, it's likely that the manager also wants to know which proportion of tickets resulted in a positive or negative outcome?

By analyzing the text within each ticket, and subsequent exchanges, customer support managers can see how each agent handled tickets, and whether customers were happy with the outcome.

Basically, the challenge in text analysis is decoding the ambiguity of human language, while in text analytics it's detecting patterns and trends from the numerical results.

Why Is Text Analysis Important?

When you put machines to work on organizing and analyzing your text data, the insights and benefits are huge.

Let's take a look at some of the advantages of text analysis, below:

Text Analysis Is Scalable

Text analysis tools allow businesses to structure vast quantities of information, like emails, chats, social media, support tickets, documents, and so on, in seconds rather than days, so you can redirect extra resources to more important business tasks.

Analyze Text in Real-time

Businesses are inundated with information and customer comments can appear anywhere on the web these days, but it can be difficult to keep an eye on it all. Text analysis is a game-changer when it comes to detecting urgent matters, wherever they may appear, 24/7 and in real time. By training text analysis models to detect expressions and sentiments that imply negativity or urgency, businesses can automatically flag tweets, reviews, videos, tickets, and the like, and take action sooner rather than later.

AI Text Analysis Delivers Consistent Criteria

Humans make errors. Fact. And the more tedious and time-consuming a task is, the more errors they make. By training text analysis models to your needs and criteria, algorithms are able to analyze, understand, and sort through data much more accurately than humans ever could.

Text Analysis Methods & Techniques

There are basic and more advanced text analysis techniques, each used for different purposes. First, learn about the simpler text analysis techniques and examples of when you might use each one.

Text Classification

Text extraction, word frequency, collocation, concordance, word sense disambiguation.

Text classification is the process of assigning predefined tags or categories to unstructured text. It's considered one of the most useful natural language processing techniques because it's so versatile and can organize, structure, and categorize pretty much any form of text to deliver meaningful data and solve problems. Natural language processing (NLP) is a machine learning technique that allows computers to break down and understand text much as a human would.

Below, we're going to focus on some of the most common text classification tasks, which include sentiment analysis, topic modeling, language detection, and intent detection.

Sentiment Analysis

Customers freely leave their opinions about businesses and products in customer service interactions, on surveys, and all over the internet. Sentiment analysis uses powerful machine learning algorithms to automatically read and classify for opinion polarity (positive, negative, neutral) and beyond, into the feelings and emotions of the writer, even context and sarcasm.

For example, by using sentiment analysis companies are able to flag complaints or urgent requests, so they can be dealt with immediately – even avert a PR crisis on social media . Sentiment classifiers can assess brand reputation, carry out market research, and help improve products with customer feedback.

Try out MonkeyLearn's pre-trained classifier . Just enter your own text to see how it works:

Test with your own text

Topic analysis.

Another common example of text classification is topic analysis (or topic modeling ) that automatically organizes text by subject or theme. For example:

“The app is really simple and easy to use”

If we are using topic categories, like Pricing, Customer Support, and Ease of Use, this product feedback would be classified under Ease of Use .

Try out MonkeyLearn's pre-trained topic classifier , which can be used to categorize NPS responses for SaaS products.

Intent Detection

Text classifiers can also be used to detect the intent of a text. Intent detection or intent classification is often used to automatically understand the reason behind customer feedback. Is it a complaint? Or is a customer writing with the intent to purchase a product? Machine learning can read chatbot conversations or emails and automatically route them to the proper department or employee.

Try out MonkeyLearn's email intent classifier .

Text extraction is another widely used text analysis technique that extracts pieces of data that already exist within any given text. You can extract things like keywords, prices, company names, and product specifications from news reports, product reviews, and more.

You can automatically populate spreadsheets with this data or perform extraction in concert with other text analysis techniques to categorize and extract data at the same time.

Keyword Extraction

Keywords are the most used and most relevant terms within a text, words and phrases that summarize the contents of text. [Keyword extraction] (]( https://monkeylearn.com/keyword-extraction/ ) can be used to index data to be searched and to generate word clouds (a visual representation of text data).

Try out MonkeyLearn's pre-trained keyword extractor to see how it works. Just type in your text below:

Entity Recognition

A named entity recognition (NER) extractor finds entities, which can be people, companies, or locations and exist within text data. Results are shown labeled with the corresponding entity label, like in MonkeyLearn's pre-trained name extractor :

Word frequency is a text analysis technique that measures the most frequently occurring words or concepts in a given text using the numerical statistic TF-IDF (term frequency-inverse document frequency).

You might apply this technique to analyze the words or expressions customers use most frequently in support conversations. For example, if the word 'delivery' appears most often in a set of negative support tickets, this might suggest customers are unhappy with your delivery service.

Collocation helps identify words that commonly co-occur. For example, in customer reviews on a hotel booking website, the words 'air' and 'conditioning' are more likely to co-occur rather than appear individually. Bigrams (two adjacent words e.g. 'air conditioning' or 'customer support') and trigrams (three adjacent words e.g. 'out of office' or 'to be continued') are the most common types of collocation you'll need to look out for.

Collocation can be helpful to identify hidden semantic structures and improve the granularity of the insights by counting bigrams and trigrams as one word.

Concordance helps identify the context and instances of words or a set of words. For example, the following is the concordance of the word “simple” in a set of app reviews:

In this case, the concordance of the word “simple” can give us a quick grasp of how reviewers are using this word. It can also be used to decode the ambiguity of the human language to a certain extent, by looking at how words are used in different contexts, as well as being able to analyze more complex phrases.

It's very common for a word to have more than one meaning, which is why word sense disambiguation is a major challenge of natural language processing. Take the word 'light' for example. Is the text referring to weight, color, or an electrical appliance? Smart text analysis with word sense disambiguation can differentiate words that have more than one meaning, but only after training models to do so.

Text clusters are able to understand and group vast quantities of unstructured data. Although less accurate than classification algorithms, clustering algorithms are faster to implement, because you don't need to tag examples to train models. That means these smart algorithms mine information and make predictions without the use of training data, otherwise known as unsupervised machine learning.

Google is a great example of how clustering works. When you search for a term on Google, have you ever wondered how it takes just seconds to pull up relevant results? Google's algorithm breaks down unstructured data from web pages and groups pages into clusters around a set of similar words or n-grams (all possible combinations of adjacent words or letters in a text). So, the pages from the cluster that contain a higher count of words or n-grams relevant to the search query will appear first within the results.

To really understand how automated text analysis works, you need to understand the basics of machine learning . Let's start with this definition from Machine Learning by Tom Mitchell :

"A computer program is said to learn to perform a task T from experience E".

In other words, if we want text analysis software to perform desired tasks, we need to teach machine learning algorithms how to analyze, understand and derive meaning from text. But how? The simple answer is by tagging examples of text. Once a machine has enough examples of tagged text to work with, algorithms are able to start differentiating and making associations between pieces of text, and make predictions by themselves.

It's very similar to the way humans learn how to differentiate between topics, objects, and emotions. Let's say we have urgent and low priority issues to deal with. We don't instinctively know the difference between them – we learn gradually by associating urgency with certain expressions.

For example, when we want to identify urgent issues, we'd look out for expressions like 'please help me ASAP!' or 'urgent: can't enter the platform, the system is DOWN!!' . On the other hand, to identify low priority issues, we'd search for more positive expressions like 'thanks for the help! Really appreciate it' or 'the new feature works like a dream' .

Text analysis can stretch it's AI wings across a range of texts depending on the results you desire. It can be applied to:

• Whole documents : obtains information from a complete document or paragraph: e.g., the overall sentiment of a customer review.
• Single sentences : obtains information from specific sentences: e.g., more detailed sentiments of every sentence of a customer review.
• Sub-sentences : obtains information from sub-expressions within a sentence: e.g., the underlying sentiments of every opinion unit of a customer review.

Once you know how you want to break up your data, you can start analyzing it.

Let’s take a look at how text analysis works, step-by-step, and go into more detail about the different machine learning algorithms and techniques available.

Data Gathering

You can gather data about your brand, product or service from both internal and external sources:

Internal Data

This is the data you generate every day, from emails and chats, to surveys, customer queries, and customer support tickets.

You just need to export it from your software or platform as a CSV or Excel file, or connect an API to retrieve it directly.

Some examples of internal data:

Customer Service Software : the software you use to communicate with customers, manage user queries and deal with customer support issues: Zendesk, Freshdesk, and Help Scout are a few examples.

CRM : software that keeps track of all the interactions with clients or potential clients. It can involve different areas, from customer support to sales and marketing. Hubspot, Salesforce, and Pipedrive are examples of CRMs.

Chat : apps that communicate with the members of your team or your customers, like Slack, Hipchat, Intercom, and Drift.

Email : the king of business communication, emails are still the most popular tool to manage conversations with customers and team members.

Surveys : generally used to gather customer service feedback, product feedback, or to conduct market research, like Typeform, Google Forms, and SurveyMonkey.

NPS (Net Promoter Score) : one of the most popular metrics for customer experience in the world. Many companies use NPS tracking software to collect and analyze feedback from their customers. A few examples are Delighted, Promoter.io and Satismeter.

Databases : a database is a collection of information. By using a database management system, a company can store, manage and analyze all sorts of data. Examples of databases include Postgres, MongoDB, and MySQL.

Product Analytics : the feedback and information about interactions of a customer with your product or service. It's useful to understand the customer's journey and make data-driven decisions. ProductBoard and UserVoice are two tools you can use to process product analytics.

External Data

This is text data about your brand or products from all over the web. You can use web scraping tools, APIs, and open datasets to collect external data from social media, news reports, online reviews, forums, and more, and analyze it with machine learning models.

Web Scraping Tools:

Visual Web Scraping Tools : you can build your own web scraper even with no coding experience, with tools like. Dexi.io, Portia, and ParseHub.e.

Web Scraping Frameworks : seasoned coders can benefit from tools, like Scrapy in Python and Wombat in Ruby, to create custom scrapers.

Facebook, Twitter, and Instagram, for example, have their own APIs and allow you to extract data from their platforms. Major media outlets like the New York Times or The Guardian also have their own APIs and you can use them to search their archive or gather users' comments, among other things.

Integrations

SaaS tools, like MonkeyLearn offer integrations with the tools you already use . You can connect directly to Twitter , Google Sheets , Gmail, Zendesk, SurveyMonkey, Rapidminer, and more. And perform text analysis on Excel data by uploading a file.

2. Data Preparation

In order to automatically analyze text with machine learning, you’ll need to organize your data. Most of this is done automatically, and you won't even notice it's happening. However, it's important to understand that automatic text analysis makes use of a number of natural language processing techniques (NLP) like the below.

Tokenization, Part-of-speech Tagging, and Parsing

Tokenization is the process of breaking up a string of characters into semantically meaningful parts that can be analyzed (e.g., words), while discarding meaningless chunks (e.g. whitespaces).

The examples below show two different ways in which one could tokenize the string 'Analyzing text is not that hard' .

(Incorrect): Analyzing text is not that hard. = [“Analyz”, “ing text”, “is n”, “ot that”, “hard.”]

(Correct): Analyzing text is not that hard. = [“Analyzing”, “text”, “is”, “not”, “that”, “hard”, “.”]

Once the tokens have been recognized, it's time to categorize them. Part-of-speech tagging refers to the process of assigning a grammatical category, such as noun, verb, etc. to the tokens that have been detected.

Here are the PoS tags of the tokens from the sentence above:

“Analyzing”: VERB, “text”: NOUN, “is”: VERB, “not”: ADV, “that”: ADV, “hard”: ADJ, “.”: PUNCT

With all the categorized tokens and a language model (i.e. a grammar), the system can now create more complex representations of the texts it will analyze. This process is known as parsing . In other words, parsing refers to the process of determining the syntactic structure of a text. To do this, the parsing algorithm makes use of a grammar of the language the text has been written in. Different representations will result from the parsing of the same text with different grammars.

The examples below show the dependency and constituency representations of the sentence 'Analyzing text is not that hard' .

Dependency Parsing

Dependency grammars can be defined as grammars that establish directed relations between the words of sentences. Dependency parsing is the process of using a dependency grammar to determine the syntactic structure of a sentence:

Constituency Parsing

Constituency phrase structure grammars model syntactic structures by making use of abstract nodes associated to words and other abstract categories (depending on the type of grammar) and undirected relations between them. Constituency parsing refers to the process of using a constituency grammar to determine the syntactic structure of a sentence:

As you can see in the images above, the output of the parsing algorithms contains a great deal of information which can help you understand the syntactic (and some of the semantic) complexity of the text you intend to analyze.

Depending on the problem at hand, you might want to try different parsing strategies and techniques. However, at present, dependency parsing seems to outperform other approaches.

Lemmatization and Stemming

Stemming and lemmatization both refer to the process of removing all of the affixes (i.e. suffixes, prefixes, etc.) attached to a word in order to keep its lexical base, also known as root or stem or its dictionary form or le mma . The main difference between these two processes is that stemming is usually based on rules that trim word beginnings and endings (and sometimes lead to somewhat weird results), whereas lemmatization makes use of dictionaries and a much more complex morphological analysis.

The table below shows the output of NLTK's Snowball Stemmer and Spacy's lemmatizer for the tokens in the sentence 'Analyzing text is not that hard' . The differences in the output have been boldfaced:

Stopword Removal

To provide a more accurate automated analysis of the text, we need to remove the words that provide very little semantic information or no meaning at all. These words are also known as stopwords: a, and, or, the, etc.

There are many different lists of stopwords for every language. However, it's important to understand that you might need to add words to or remove words from those lists depending on the texts you want to analyze and the analyses you would like to perform.

You might want to do some kind of lexical analysis of the domain your texts come from in order to determine the words that should be added to the stopwords list.

Analyze Your Text Data

Now that you’ve learned how to mine unstructured text data and the basics of data preparation, how do you analyze all of this text?

Well, the analysis of unstructured text is not straightforward. There are countless text analysis methods, but two of the main techniques are text classification and text extraction .

Text classification (also known as text categorization or text tagging ) refers to the process of assigning tags to texts based on its content.

In the past, text classification was done manually, which was time-consuming, inefficient, and inaccurate. But automated machine learning text analysis models often work in just seconds with unsurpassed accuracy.

The most popular text classification tasks include sentiment analysis (i.e. detecting when a text says something positive or negative about a given topic), topic detection (i.e. determining what topics a text talks about), and intent detection (i.e. detecting the purpose or underlying intent of the text), among others, but there are a great many more applications you might be interested in.

Rule-based Systems

In text classification, a rule is essentially a human-made association between a linguistic pattern that can be found in a text and a tag. Rules usually consist of references to morphological, lexical, or syntactic patterns, but they can also contain references to other components of language, such as semantics or phonology.

Here's an example of a simple rule for classifying product descriptions according to the type of product described in the text:

(HDD|RAM|SSD|Memory) → Hardware

In this case, the system will assign the Hardware tag to those texts that contain the words HDD , RAM , SSD , or Memory .

The most obvious advantage of rule-based systems is that they are easily understandable by humans. However, creating complex rule-based systems takes a lot of time and a good deal of knowledge of both linguistics and the topics being dealt with in the texts the system is supposed to analyze.

On top of that, rule-based systems are difficult to scale and maintain because adding new rules or modifying the existing ones requires a lot of analysis and testing of the impact of these changes on the results of the predictions.

Machine Learning-based Systems

Machine learning-based systems can make predictions based on what they learn from past observations. These systems need to be fed multiple examples of texts and the expected predictions (tags) for each. This is called training data . The more consistent and accurate your training data, the better ultimate predictions will be.

When you train a machine learning-based classifier, 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 system can extract relevant features (pieces of information) which will help it learn from the existing data and make predictions about the texts to come.

There are a number of ways to do this, but one of the most frequently used is called bag of words vectorization . You can learn more about vectorization here .

Once the texts have been transformed into vectors, they are fed into a machine learning algorithm together with their expected output to create a classification model that can choose what features best represent the texts and make predictions about unseen texts:

The trained model will transform unseen text into a vector, extract its relevant features, and make a prediction:

Machine Learning Algorithms

There are many machine learning algorithms used in text classification. The most frequently used are the Naive Bayes (NB) family of algorithms, Support Vector Machines (SVM), and deep learning algorithms.

The Naive Bayes family of algorithms is based on Bayes's Theorem and the conditional probabilities of occurrence of the words of a sample text within the words of a set of texts that belong to a given tag. Vectors that represent texts encode information about how likely it is for the words in the text to occur in the texts of a given tag. With this information, the probability of a text's belonging to any given tag in the model can be computed. Once all of the probabilities have been computed for an input text, the classification model will return the tag with the highest probability as the output for that input.

One of the main advantages of this algorithm is that results can be quite good even if there’s not much training data.

Support Vector Machines (SVM) is an algorithm that can divide a vector space of tagged texts into two subspaces: one space that contains most of the vectors that belong to a given tag and another subspace that contains most of the vectors that do not belong to that one tag.

Classification models that use SVM at their core will transform texts into vectors and will determine what side of the boundary that divides the vector space for a given tag those vectors belong to. Based on where they land, the model will know if they belong to a given tag or not.

The most important advantage of using SVM is that results are usually better than those obtained with Naive Bayes. However, more computational resources are needed for SVM.

Deep Learning is a set of algorithms and techniques that use “artificial neural networks” to process data much as the human brain does. These algorithms use huge amounts of training data (millions of examples) to generate semantically rich representations of texts which can then be fed into machine learning-based models of different kinds that will make much more accurate predictions than traditional machine learning models:

Hybrid Systems

Hybrid systems usually contain machine learning-based systems at their cores and rule-based systems to improve the predictions

Classifier performance is usually evaluated through standard metrics used in the machine learning field: accuracy , precision , recall , and F1 score . Understanding what they mean will give you a clearer idea of how good your classifiers are at analyzing your texts.

It is also important to understand that evaluation can be performed over a fixed testing set (i.e. a set of texts for which we know the expected output tags) or by using cross-validation (i.e. a method that splits your training data into different folds so that you can use some subsets of your data for training purposes and some for testing purposes, see below ).

Accuracy, Precision, Recall, and F1 score

Accuracy is the number of correct predictions the classifier has made divided by the total number of predictions. In general, accuracy alone is not a good indicator of performance. For example, when categories are imbalanced, that is, when there is one category that contains many more examples than all of the others, predicting all texts as belonging to that category will return high accuracy levels. This is known as the accuracy paradox . To get a better idea of the performance of a classifier, you might want to consider precision and recall instead.

Precision states how many texts were predicted correctly out of the ones that were predicted as belonging to a given tag. In other words, precision takes the number of texts that were correctly predicted as positive for a given tag and divides it by the number of texts that were predicted (correctly and incorrectly) as belonging to the tag.

We have to bear in mind that precision only gives information about the cases where the classifier predicts that the text belongs to a given tag. This might be particularly important, for example, if you would like to generate automated responses for user messages. In this case, before you send an automated response you want to know for sure you will be sending the right response, right? In other words, if your classifier says the user message belongs to a certain type of message, you would like the classifier to make the right guess. This means you would like a high precision for that type of message.

Recall states how many texts were predicted correctly out of the ones that should have been predicted as belonging to a given tag. In other words, recall takes the number of texts that were correctly predicted as positive for a given tag and divides it by the number of texts that were either predicted correctly as belonging to the tag or that were incorrectly predicted as not belonging to the tag.

Recall might prove useful when routing support tickets to the appropriate team, for example. It might be desired for an automated system to detect as many tickets as possible for a critical tag (for example tickets about 'Outrages / Downtime' ) at the expense of making some incorrect predictions along the way. In this case, making a prediction will help perform the initial routing and solve most of these critical issues ASAP. If the prediction is incorrect, the ticket will get rerouted by a member of the team. When processing thousands of tickets per week, high recall (with good levels of precision as well, of course) can save support teams a good deal of time and enable them to solve critical issues faster.

The F1 score is the harmonic means of precision and recall. It tells you how well your classifier performs if equal importance is given to precision and recall. In general, F1 score is a much better indicator of classifier performance than accuracy is.

Cross-validation

Cross-validation is quite frequently used to evaluate the performance of text classifiers. The method is simple. First of all, the training dataset is randomly split into a number of equal-length subsets (e.g. 4 subsets with 25% of the original data each). Then, all the subsets except for one are used to train a classifier (in this case, 3 subsets with 75% of the original data) and this classifier is used to predict the texts in the remaining subset. Next, all the performance metrics are computed (i.e. accuracy, precision, recall, F1, etc.). Finally, the process is repeated with a new testing fold until all the folds have been used for testing purposes.

Once all folds have been used, the average performance metrics are computed and the evaluation process is finished.

Text Extraction refers to the process of recognizing structured pieces of information from unstructured text. For example, it can be useful to automatically detect the most relevant keywords from a piece of text, identify names of companies in a news article, detect lessors and lessees in a financial contract, or identify prices on product descriptions.

Regular Expressions

Regular Expressions (a.k.a. regexes) work as the equivalent of the rules defined in classification tasks. In this case, a regular expression defines a pattern of characters that will be associated with a tag.

For example, the pattern below will detect most email addresses in a text if they preceded and followed by spaces:

(?i)\b(?: [a-zA-Z0-9_ - .] +)@(?:(?: [ [0-9] {1,3} . [0-9] {1,3} . [0-9] {1,3} . )|(?:(?: [a-zA-Z0-9 -] + . )+))(?: [a-zA-Z] {2,4}| [0-9] {1,3})(?: ] ?)\b

By detecting this match in texts and assigning it the email tag, we can create a rudimentary email address extractor.

There are obvious pros and cons of this approach. On the plus side, you can create text extractors quickly and the results obtained can be good, provided you can find the right patterns for the type of information you would like to detect. On the minus side, regular expressions can get extremely complex and might be really difficult to maintain and scale, particularly when many expressions are needed in order to extract the desired patterns.

Conditional Random Fields

Conditional Random Fields (CRF) is a statistical approach often used in machine-learning-based text extraction. This approach learns the patterns to be extracted by weighing a set of features of the sequences of words that appear in a text. Through the use of CRFs, we can add multiple variables which depend on each other to the patterns we use to detect information in texts, such as syntactic or semantic information.

This usually generates much richer and complex patterns than using regular expressions and can potentially encode much more information. However, more computational resources are needed in order to implement it since all the features have to be calculated for all the sequences to be considered and all of the weights assigned to those features have to be learned before determining whether a sequence should belong to a tag or not.

One of the main advantages of the CRF approach is its generalization capacity. Once an extractor has been trained using the CRF approach over texts of a specific domain, it will have the ability to generalize what it has learned to other domains reasonably well.

Extractors are sometimes evaluated by calculating the same standard performance metrics we have explained above for text classification, namely, accuracy , precision , recall , and F1 score . However, these metrics do not account for partial matches of patterns. In order for an extracted segment to be a true positive for a tag, it has to be a perfect match with the segment that was supposed to be extracted.

Consider the following example:

'Your flight will depart on January 14, 2020 at 03:30 PM from SFO'

If we created a date extractor, we would expect it to return January 14, 2020 as a date from the text above, right? So, if the output of the extractor were January 14, 2020, we would count it as a true positive for the tag DATE .

But, what if the output of the extractor were January 14? Would you say the extraction was bad? Would you say it was a false positive for the tag DATE ? To capture partial matches like this one, some other performance metrics can be used to evaluate the performance of extractors. One example of this is the ROUGE family of metrics.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a family of metrics used in the fields of machine translation and automatic summarization that can also be used to assess the performance of text extractors. These metrics basically compute the lengths and number of sequences that overlap between the source text (in this case, our original text) and the translated or summarized text (in this case, our extraction).

Depending on the length of the units whose overlap you would like to compare, you can define ROUGE-n metrics (for units of length n ) or you can define the ROUGE-LCS or ROUGE-L metric if you intend to compare the longest common sequence (LCS).

4.Visualize Your Text Data

Now you know a variety of text analysis methods to break down your data, but what do you do with the results? Business intelligence (BI) and data visualization tools make it easy to understand your results in striking dashboards.

• MonkeyLearn Studio

MonkeyLearn Studio is an all-in-one data gathering, analysis, and visualization tool. Deep learning machine learning techniques allow you to choose the text analyses you need (keyword extraction, sentiment analysis, aspect classification, and on and on) and chain them together to work simultaneously.

You’ll see the importance of text analytics right away. Simply upload your data and visualize the results for powerful insights. It all works together in a single interface, so you no longer have to upload and download between applications.

• Google Data Studio

Google's free visualization tool allows you to create interactive reports using a wide variety of data. Once you've imported your data you can use different tools to design your report and turn your data into an impressive visual story. Share the results with individuals or teams, publish them on the web, or embed them on your website.

Looker is a business data analytics platform designed to direct meaningful data to anyone within a company. The idea is to allow teams to have a bigger picture about what's happening in their company.

You can connect to different databases and automatically create data models, which can be fully customized to meet specific needs. Take a look here to get started.

Tableau is a business intelligence and data visualization tool with an intuitive, user-friendly approach (no technical skills required). Tableau allows organizations to work with almost any existing data source and provides powerful visualization options with more advanced tools for developers.

There's a trial version available for anyone wanting to give it a go. Learn how to perform text analysis in Tableau .

Text Analysis Applications & Examples

Did you know that 80% of business data is text? Text is present in every major business process, from support tickets, to product feedback, and online customer interactions. Automated, real time text analysis can help you get a handle on all that data with a broad range of business applications and use cases. Maximize efficiency and reduce repetitive tasks that often have a high turnover impact. Better understand customer insights without having to sort through millions of social media posts, online reviews, and survey responses.

If you work in customer experience, product, marketing, or sales, there are a number of text analysis applications to automate processes and get real world insights. And best of all you don’t need any data science or engineering experience to do it.

Social Media Monitoring

Let's say you work for Uber and you want to know what users are saying about the brand. You've read some positive and negative feedback on Twitter and Facebook. But 500 million tweets are sent each day , and Uber has thousands of mentions on social media every month. Can you imagine analyzing all of them manually?

This is where sentiment analysis comes in to analyze the opinion of a given text. By analyzing your social media mentions with a sentiment analysis model , you can automatically categorize them into Positive , Neutral or Negative . Then run them through a topic analyzer to understand the subject of each text. By running aspect-based sentiment analysis , you can automatically pinpoint the reasons behind positive or negative mentions and get insights such as:

• The top complaint about Uber on social media?
• The success rate of Uber's customer service - are people happy or are annoyed with it?
• What Uber users like about the service when they mention Uber in a positive way?

Now, let's say you've just added a new service to Uber. For example, Uber Eats. It's a crucial moment, and your company wants to know what people are saying about Uber Eats so that you can fix any glitches as soon as possible, and polish the best features. You can also use aspect-based sentiment analysis on your Facebook, Instagram and Twitter profiles for any Uber Eats mentions and discover things such as:

• Are people happy with Uber Eats so far?
• What is the most urgent issue to fix?
• How can we incorporate positive stories into our marketing and PR communication?

Not only can you use text analysis to keep tabs on your brand's social media mentions, but you can also use it to monitor your competitors' mentions as well. Is a client complaining about a competitor's service? That gives you a chance to attract potential customers and show them how much better your brand is.

Brand Monitoring

Follow comments about your brand in real time wherever they may appear (social media, forums, blogs, review sites, etc.). You’ll know when something negative arises right away and be able to use positive comments to your advantage.

The power of negative reviews is quite strong: 40% of consumers are put off from buying if a business has negative reviews. An angry customer complaining about poor customer service can spread like wildfire within minutes: a friend shares it, then another, then another… And before you know it, the negative comments have gone viral.

• Understand how your brand reputation evolves over time.
• Compare your brand reputation to your competitor's.
• Identify which aspects are damaging your reputation.
• Pinpoint which elements are boosting your brand reputation on online media.
• Identify potential PR crises so you can deal with them ASAP.
• Tune into data from a specific moment, like the day of a new product launch or IPO filing. Just run a sentiment analysis on social media and press mentions on that day, to find out what people said about your brand.
• Repost positive mentions of your brand to get the word out.

Customer Service

Despite many people's fears and expectations, text analysis doesn't mean that customer service will be entirely machine-powered. It just means that businesses can streamline processes so that teams can spend more time solving problems that require human interaction. That way businesses will be able to increase retention, given that 89 percent of customers change brands because of poor customer service. But, how can text analysis assist your company's customer service?

Ticket Tagging

Let machines do the work for you. Text analysis automatically identifies topics, and tags each ticket. Here's how it works:

• The model analyzes the language and expressions a customer language, for example, “I didn't get the right order.”
• Then, it compares it to other similar conversations.
• Finally, it finds a match and tags the ticket automatically. In this case, it could be under a Shipping Problems tag.

This happens automatically, whenever a new ticket comes in, freeing customer agents to focus on more important tasks.

Ticket Routing & Triage: Find the Right Person for the Job

Machine learning can read a ticket for subject or urgency, and automatically route it to the appropriate department or employee .

For example, for a SaaS company that receives a customer ticket asking for a refund, the text mining system will identify which team usually handles billing issues and send the ticket to them. If a ticket says something like “How can I integrate your API with python?” , it would go straight to the team in charge of helping with Integrations.

Ticket Analytics: Learn More From Your Customers

What is commonly assessed to determine the performance of a customer service team? Common KPIs are first response time , average time to resolution (i.e. how long it takes your team to resolve issues), and customer satisfaction (CSAT). And, let's face it, overall client satisfaction has a lot to do with the first two metrics.

But how do we get actual CSAT insights from customer conversations? How can we identify if a customer is happy with the way an issue was solved? Or if they have expressed frustration with the handling of the issue?

In this situation, aspect-based sentiment analysis could be used. This type of text analysis delves into the feelings and topics behind the words on different support channels, such as support tickets, chat conversations, emails, and CSAT surveys. A text analysis model can understand words or expressions to define the support interaction as Positive , Negative , or Neutral , understand what was mentioned (e.g. Service or UI/UX ), and even determine the sentiments behind the words (e.g. Sadness , Anger , etc.).

Urgency Detection: Prioritize Urgent Tickets

“Where do I start?” is a question most customer service representatives often ask themselves. Urgency is definitely a good starting point, but how do we define the level of urgency without wasting valuable time deliberating?

Text mining software can define the urgency level of a customer ticket and tag it accordingly. Support tickets with words and expressions that denote urgency, such as 'as soon as possible' or 'right away' , are duly tagged as Priority .

To see how text analysis works to detect urgency, check out this MonkeyLearn urgency detection demo model .

Voice of Customer (VoC) & Customer Feedback

Once you get a customer, retention is key, since acquiring new clients is five to 25 times more expensive than retaining the ones you already have. That's why paying close attention to the voice of the customer can give your company a clear picture of the level of client satisfaction and, consequently, of client retention. Also, it can give you actionable insights to prioritize the product roadmap from a customer's perspective.

Analyzing NPS Responses

Maybe your brand already has a customer satisfaction survey in place, the most common one being the Net Promoter Score (NPS). This survey asks the question, 'How likely is it that you would recommend [brand] to a friend or colleague?' . The answer is a score from 0-10 and the result is divided into three groups: the promoters , the passives , and the detractors .

But here comes the tricky part: there's an open-ended follow-up question at the end 'Why did you choose X score?' The answer can provide your company with invaluable insights. Without the text, you're left guessing what went wrong. And, now, with text analysis, you no longer have to read through these open-ended responses manually.

You can do what Promoter.io did: extract the main keywords of your customers' feedback to understand what's being praised or criticized about your product. Is the keyword 'Product' mentioned mostly by promoters or detractors? With this info, you'll be able to use your time to get the most out of NPS responses and start taking action.

Another option is following in Retently's footsteps using text analysis to classify your feedback into different topics, such as Customer Support, Product Design, and Product Features, then analyze each tag with sentiment analysis to see how positively or negatively clients feel about each topic. Now they know they're on the right track with product design, but still have to work on product features.

Analyzing Customer Surveys

Does your company have another customer survey system? If it's a scoring system or closed-ended questions, it'll be a piece of cake to analyze the responses: just crunch the numbers.

However, if you have an open-text survey, whether it's provided via email or it's an online form, you can stop manually tagging every single response by letting text analysis do the job for you. Besides saving time, you can also have consistent tagging criteria without errors, 24/7.

Business Intelligence

Data analysis is at the core of every business intelligence operation. Now, what can a company do to understand, for instance, sales trends and performance over time? With numeric data, a BI team can identify what's happening (such as sales of X are decreasing) – but not why . Numbers are easy to analyze, but they are also somewhat limited. Text data, on the other hand, is the most widespread format of business information and can provide your organization with valuable insight into your operations. Text analysis with machine learning can automatically analyze this data for immediate insights.

For example, you can run keyword extraction and sentiment analysis on your social media mentions to understand what people are complaining about regarding your brand.

You can also run aspect-based sentiment analysis on customer reviews that mention poor customer experiences. After all, 67% of consumers list bad customer experience as one of the primary reasons for churning. Maybe it's bad support, a faulty feature, unexpected downtime, or a sudden price change. Analyzing customer feedback can shed a light on the details, and the team can take action accordingly.

And what about your competitors? What are their reviews saying? Run them through your text analysis model and see what they're doing right and wrong and improve your own decision-making.

Sales and Marketing

Prospecting is the most difficult part of the sales process. And it's getting harder and harder. The sales team always want to close deals, which requires making the sales process more efficient. But 27% of sales agents are spending over an hour a day on data entry work instead of selling, meaning critical time is lost to administrative work and not closing deals.

Text analysis takes the heavy lifting out of manual sales tasks, including:

• Updating the deal status as 'Not interested' in your CRM.
• Qualifying your leads based on company descriptions.
• Identifying leads on social media that express buying intent.

GlassDollar , a company that links founders to potential investors, is using text analysis to find the best quality matches. How? They use text analysis to classify companies using their company descriptions. The results? They saved themselves days of manual work, and predictions were 90% accurate after training a text classification model. You can learn more about their experience with MonkeyLearn here .

Not only can text analysis automate manual and tedious tasks, but it can also improve your analytics to make the sales and marketing funnels more efficient. For example, you can automatically analyze the responses from your sales emails and conversations to understand, let's say, a drop in sales:

• What are the blocks to completing a deal?
• What sparks a customer's interest?
• What are customer concerns?

Now, Imagine that your sales team's goal is to target a new segment for your SaaS: people over 40. The first impression is that they don't like the product, but why ? Just filter through that age group's sales conversations and run them on your text analysis model. Sales teams could make better decisions using in-depth text analysis on customer conversations.

Finally, you can use machine learning and text analysis to provide a better experience overall within your sales process. For example, Drift , a marketing conversational platform, integrated MonkeyLearn API to allow recipients to automatically opt out of sales emails based on how they reply.

It's time to boost sales and stop wasting valuable time with leads that don't go anywhere. Xeneta, a sea freight company, developed a machine learning algorithm and trained it to identify which companies were potential customers, based on the company descriptions gathered through FullContact (a SaaS company that has descriptions of millions of companies).

You can do the same or target users that visit your website to:

• Get information about where potential customers work using a service like Clearbit and classify the company according to its type of business to see if it's a possible lead.
• Extract information to easily learn the user's job position, the company they work for, its type of business and other relevant information.
• Hone in on the most qualified leads and save time actually looking for them: sales reps will receive the information automatically and start targeting the potential customers right away.

Product Analytics

Let's imagine your startup has an app on the Google Play store. You're receiving some unusually negative comments. What's going on?

You can find out what’s happening in just minutes by using a text analysis model that groups reviews into different tags like Ease of Use and Integrations. Then run them through a sentiment analysis model to find out whether customers are talking about products positively or negatively. Finally, graphs and reports can be created to visualize and prioritize product problems with MonkeyLearn Studio .

We did this with reviews for Slack from the product review site Capterra and got some pretty interesting insights . Here's how:

We analyzed reviews with aspect-based sentiment analysis and categorized them into main topics and sentiment.

We extracted keywords with the keyword extractor to get some insights into why reviews that are tagged under 'Performance-Quality-Reliability' tend to be negative.

Text Analysis Resources

There are a number of valuable resources out there to help you get started with all that text analysis has to offer.

Text Analysis APIs

You can use open-source libraries or SaaS APIs to build a text analysis solution that fits your needs. Open-source libraries require a lot of time and technical know-how, while SaaS tools can often be put to work right away and require little to no coding experience.

Open Source Libraries

Python is the most widely-used language in scientific computing, period. Tools like NumPy and SciPy have established it as a fast, dynamic language that calls C and Fortran libraries where performance is needed.

These things, combined with a thriving community and a diverse set of libraries to implement natural language processing (NLP) models has made Python one of the most preferred programming languages for doing text analysis.

NLTK , the Natural Language Toolkit, is a best-of-class library for text analysis tasks. NLTK is used in many university courses, so there's plenty of code written with it and no shortage of users familiar with both the library and the theory of NLP who can help answer your questions.

SpaCy is an industrial-strength statistical NLP library. Aside from the usual features, it adds deep learning integration and convolutional neural network models for multiple languages.

Unlike NLTK, which is a research library, SpaCy aims to be a battle-tested, production-grade library for text analysis.

Scikit-learn

Scikit-learn is a complete and mature machine learning toolkit for Python built on top of NumPy, SciPy, and matplotlib, which gives it stellar performance and flexibility for building text analysis models.

Developed by Google, TensorFlow is by far the most widely used library for distributed deep learning. Looking at this graph we can see that TensorFlow is ahead of the competition:

PyTorch is a deep learning platform built by Facebook and aimed specifically at deep learning. PyTorch is a Python-centric library, which allows you to define much of your neural network architecture in terms of Python code, and only internally deals with lower-level high-performance code.

Keras is a widely-used deep learning library written in Python. It's designed to enable rapid iteration and experimentation with deep neural networks, and as a Python library, it's uniquely user-friendly.

An important feature of Keras is that it provides what is essentially an abstract interface to deep neural networks. The actual networks can run on top of Tensorflow, Theano, or other backends. This backend independence makes Keras an attractive option in terms of its long-term viability.

The permissive MIT license makes it attractive to businesses looking to develop proprietary models.

R is the pre-eminent language for any statistical task. Its collection of libraries (13,711 at the time of writing on CRAN far surpasses any other programming language capabilities for statistical computing and is larger than many other ecosystems. In short, if you choose to use R for anything statistics-related, you won't find yourself in a situation where you have to reinvent the wheel, let alone the whole stack.

Caret is an R package designed to build complete machine learning pipelines, with tools for everything from data ingestion and preprocessing, feature selection, and tuning your model automatically.

The Machine Learning in R project (mlr for short) provides a complete machine learning toolkit for the R programming language that's frequently used for text analysis.

Java needs no introduction. The language boasts an impressive ecosystem that stretches beyond Java itself and includes the libraries of other The JVM languages such as The Scala and Clojure . Beyond that, the JVM is battle-tested and has had thousands of person-years of development and performance tuning, so Java is likely to give you best-of-class performance for all your text analysis NLP work.

Stanford's CoreNLP project provides a battle-tested, actively maintained NLP toolkit. While it's written in Java, it has APIs for all major languages, including Python, R, and Go.

The Apache OpenNLP project is another machine learning toolkit for NLP. It can be used from any language on the JVM platform.

Weka is a GPL-licensed Java library for machine learning, developed at the University of Waikato in New Zealand. In addition to a comprehensive collection of machine learning APIs, Weka has a graphical user interface called the Explorer , which allows users to interactively develop and study their models.

Weka supports extracting data from SQL databases directly, as well as deep learning through the deeplearning4j framework.

Using a SaaS API for text analysis has a lot of advantages:

Most SaaS tools are simple plug-and-play solutions with no libraries to install and no new infrastructure.

SaaS APIs provide ready to use solutions. You give them data and they return the analysis. Every other concern – performance, scalability, logging, architecture, tools, etc. – is offloaded to the party responsible for maintaining the API.

You often just need to write a few lines of code to call the API and get the results back.

• Easy Integration:

SaaS APIs usually provide ready-made integrations with tools you may already use. This will allow you to build a truly no-code solution. Learn how to integrate text analysis with Google Sheets .

Some of the most well-known SaaS solutions and APIs for text analysis include:

• MonkeyLearn
• Google Cloud NLP
• MeaningCloud
• Amazon Comprehend

There is an ongoing Build vs. Buy Debate when it comes to text analysis applications: build your own tool with open-source software, or use a SaaS text analysis tool?

Building your own software from scratch can be effective and rewarding if you have years of data science and engineering experience, but it’s time-consuming and can cost in the hundreds of thousands of dollars.

SaaS tools, on the other hand, are a great way to dive right in. They can be straightforward, easy to use, and just as powerful as building your own model from scratch. MonkeyLearn is a SaaS text analysis platform with dozens of pre-trained models. Or you can customize your own, often in only a few steps for results that are just as accurate. All with no coding experience necessary.

Training Datasets

If you talk to any data science professional, they'll tell you that the true bottleneck to building better models is not new and better algorithms, but more data.

Indeed, in machine learning data is king: a simple model, given tons of data, is likely to outperform one that uses every trick in the book to turn every bit of training data into a meaningful response.

So, here are some high-quality datasets you can use to get started:

Topic Classification

Reuters news dataset : one the most popular datasets for text classification; it has thousands of articles from Reuters tagged with 135 categories according to their topics, such as Politics, Economics, Sports, and Business.

20 Newsgroups : a very well-known dataset that has more than 20k documents across 20 different topics.

Product reviews : a dataset with millions of customer reviews from products on Amazon.

Twitter airline sentiment on Kaggle : another widely used dataset for getting started with sentiment analysis. It contains more than 15k tweets about airlines (tagged as positive, neutral, or negative).

First GOP Debate Twitter Sentiment : another useful dataset with more than 14,000 labeled tweets (positive, neutral, and negative) from the first GOP debate in 2016.

Other Popular Datasets

Spambase : this dataset contains 4,601 emails tagged as spam and not spam.

SMS Spam Collection : another dataset for spam detection. It has more than 5k SMS messages tagged as spam and not spam.

Hate speech and offensive language : a dataset with more than 24k tagged tweets grouped into three tags: clean, hate speech, and offensive language.

Finding high-volume and high-quality training datasets are the most important part of text analysis, more important than the choice of the programming language or tools for creating the models. Remember, the best-architected machine-learning pipeline is worthless if its models are backed by unsound data.

Text Analysis Tutorials

The best way to learn is by doing.

First, we'll go through programming-language-specific tutorials using open-source tools for text analysis. These will help you deepen your understanding of the available tools for your platform of choice.

Then, we'll take a step-by-step tutorial of MonkeyLearn so you can get started with text analysis right away.

Tutorials Using Open Source Libraries

In this section, we'll look at various tutorials for text analysis in the main programming languages for machine learning that we listed above.

The official NLTK book is a complete resource that teaches you NLTK from beginning to end. In addition, the reference documentation is a useful resource to consult during development.

Other useful tutorials include:

WordNet with NLTK: Finding Synonyms for words in Python : this tutorial shows you how to build a thesaurus using Python and WordNet .

Tokenizing Words and Sentences with NLTK : this tutorial shows you how to use NLTK's language models to tokenize words and sentences.

spaCy 101: Everything you need to know : part of the official documentation, this tutorial shows you everything you need to know to get started using SpaCy.

This tutorial shows you how to build a WordNet pipeline with SpaCy.

Furthermore, there's the official API documentation , which explains the architecture and API of SpaCy.

If you prefer long-form text, there are a number of books about or featuring SpaCy:

• Introduction to Machine Learning with Python: A Guide for Data Scientists .
• Practical Machine Learning with Python .
• Text Analytics with Python .

The official scikit-learn documentation contains a number of tutorials on the basic usage of scikit-learn, building pipelines, and evaluating estimators.

Scikit-learn Tutorial: Machine Learning in Python shows you how to use scikit-learn and Pandas to explore a dataset, visualize it, and train a model.

For readers who prefer books, there are a couple of choices:

Our very own Raúl Garreta wrote this book: Learning scikit-learn: Machine Learning in Python .

Additionally, the book Hands-On Machine Learning with Scikit-Learn and TensorFlow introduces the use of scikit-learn in a deep learning context.

The official Keras website has extensive API as well as tutorial documentation. For readers who prefer long-form text, the Deep Learning with Keras book is the go-to resource. The book uses real-world examples to give you a strong grasp of Keras.

Other tutorials:

Practical Text Classification With Python and Keras : this tutorial implements a sentiment analysis model using Keras, and teaches you how to train, evaluate, and improve that model.

Text Classification in Keras : this article builds a simple text classifier on the Reuters news dataset. It classifies the text of an article into a number of categories such as sports, entertainment, and technology.

TensorFlow Tutorial For Beginners introduces the mathematics behind TensorFlow and includes code examples that run in the browser, ideal for exploration and learning. The goal of the tutorial is to classify street signs.

The book Hands-On Machine Learning with Scikit-Learn and TensorFlow helps you build an intuitive understanding of machine learning using TensorFlow and scikit-learn.

Finally, there's the official Get Started with TensorFlow guide.

The official Get Started Guide from PyTorch shows you the basics of PyTorch. If you're interested in something more practical, check out this chatbot tutorial ; it shows you how to build a chatbot using PyTorch.

The Deep Learning for NLP with PyTorch tutorial is a gentle introduction to the ideas behind deep learning and how they are applied in PyTorch.

Finally, the official API reference explains the functioning of each individual component.

A Short Introduction to the Caret Package shows you how to train and visualize a simple model. A Practical Guide to Machine Learning in R shows you how to prepare data, build and train a model, and evaluate its results. Finally, you have the official documentation which is super useful to get started with Caret.

For those who prefer long-form text, on arXiv we can find an extensive mlr tutorial paper . This is closer to a book than a paper and has extensive and thorough code samples for using mlr.

If interested in learning about CoreNLP, you should check out Linguisticsweb.org's tutorial which explains how to quickly get started and perform a number of simple NLP tasks from the command line. Moreover, this CloudAcademy tutorial shows you how to use CoreNLP and visualize its results. You can also check out this tutorial specifically about sentiment analysis with CoreNLP . Finally, there's this tutorial on using CoreNLP with Python that is useful to get started with this framework.

First things first: the official Apache OpenNLP Manual should be the starting point. The book Taming Text was written by an OpenNLP developer and uses the framework to show the reader how to implement text analysis. Moreover, this tutorial takes you on a complete tour of OpenNLP, including tokenization, part of speech tagging, parsing sentences, and chunking.

The Weka library has an official book Data Mining: Practical Machine Learning Tools and Techniques that comes handy for getting your feet wet with Weka.

If you prefer videos to text, there are also a number of MOOCs using Weka:

Data Mining with Weka : this is an introductory course to Weka.

More Data Mining with Weka : this course involves larger datasets and a more complete text analysis workflow.

Advanced Data Mining with Weka : this course focuses on packages that extend Weka's functionality.

The Text Mining in WEKA Cookbook provides text-mining-specific instructions for using Weka.

How to Run Your First Classifier in Weka : shows you how to install Weka, run it, run a classifier on a sample dataset, and visualize its results.

Text Analysis Tutorial With MonkeyLearn Templates

MonkeyLearn Templates is a simple and easy-to-use platform that you can use without adding a single line of code.

Follow the step-by-step tutorial below to see how you can run your data through text analysis tools and visualize the results: 

1. Choose a template to create your workflow:

2. Upload your data.

We chose the app review template, so we’re using a dataset of reviews.

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:

• created_at: Date that the response was sent.
• text: Text of the response.
• rating: Score given by the customer.

4. Name your workflow:

5. Wait for MonkeyLearn to process your data:

6. Explore your dashboard!

MonkeyLearn’s data visualization tools make it easy to understand your results in striking dashboards. Spot patterns, trends, and immediately actionable insights in broad strokes or minute detail.

• Filter by topic, sentiment, keyword, or rating.
• Share via email with other coworkers.

Text analysis is no longer an exclusive, technobabble topic for software engineers with machine learning experience. It has become a powerful tool that helps businesses across every industry gain useful, actionable insights from their text data. Saving time, automating tasks and increasing productivity has never been easier, allowing businesses to offload cumbersome tasks and help their teams provide a better service for their customers.

If you would like to give text analysis a go, sign up to MonkeyLearn for free and begin training your very own text classifiers and extractors – no coding needed thanks to our user-friendly interface and integrations.

And take a look at the MonkeyLearn Studio public dashboard to see what data visualization can do to see your results in broad strokes or super minute detail.

Reach out to our team if you have any doubts or questions about text analysis and machine learning, and we'll help you get started!

MonkeyLearn Inc. All rights reserved 2024

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How to Read XML Files into Python

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In this article, you’ll learn what an XML file is, what they are used for, and how to read XML into Python using a few different libraries.

The ability to extract information from various file formats is a crucial data analysis skill. This is no different with an XML file: XML is a common file format in data processing, particularly when you’re dealing with data received from an API. If you're a novice data analyst venturing into the Python ecosystem, mastering the art of reading XML into Python can significantly enhance your skill set.

In this beginner-friendly guide, we will walk through the process of reading XML files into Python. We will start by explaining what XML files are and how they structure data. Afterwards, we will learn how to read XML data into Python using a few simple methods.

If you are interested in learning more about other data formats and how to process them in Python, we recommend our dedicated track on Data Processing with Python . With five interactive courses and over 35 hours of content, this track empowers you to work with some of the most common data formats you’ll encounter as a data analyst – like CSV, JSON, and Excel files.

But for now, let's find out how to work with XML in Python!

What’s in an XML File?

XML (short for Extensible Markup Language ) is a plain text file format designed to store and transport data. Its main feature is that data is stored in an arbitrary hierarchy. This is a direct contrast to other plain text formats that store data, such as a CSV table (which you can read about in this article on CSV files in Python ).

As the name suggests, XML is a markup language, so you can expect data to be stored in tags like this: <tag>. In XML, tags are often referred to as elements . HTML ( HyperText Markup Language ) is another popular markup file format, but unlike XML it is primarily used for displaying data on web pages. XML focuses on describing the structure and content of data in a hierarchical, customizable format .

XML File Structure

XML files consist of hierarchical structures organized into elements and contents . These elements encapsulate the data and provide a way to represent relationships between different pieces of information.

This is not unlike Python dictionaries, which store elements as key-value pairs. In this analogy, the dictionary key is the element in the XML file, and the dictionary value is the XML content.

Consider the following XML file that store information about books:

In this XML file, everything is contained inside the <library> element: the element is “opened” in the very first line, and is then “closed” at the very end by writing </library> . This means that <library> is the root element of this XML file.

XML File Hierarchy

The <library> root element contains multiple <book> elements. In turn, each <book> element contains the <title> , <author> , <genre> , and <year> elements, storing different information of each book as content in the XML.

Elements in the XML naturally compose a parent-child hierarchy . In this example, we could say that the <library> element is the parent of <book> , or that the <title> and <year> elements are the children of <book> . This hierarchical structure of libraries containing books, which themselves contain information of the book’s title, author, genre, and year, is the type of information that XML files can effectively store and relay.

Additionally, note how the hierarchy is created by simply opening and closing the elements at certain parts of the XML file. This simple mechanism allows us to store arbitrary hierarchical structures into the XML, even if they include hundreds of elements. In fact, formats such as XLSX use XML under the hood in order to manage all the complexity behind an Excel spreadsheet. (This article shows how to read Excel files in Python directly).

XML files can be further customized by passing attributes and values inside an element’s tag. For example, we could add two arbitrary attributes ( attr1 and attr2 ) and their respective values to the <book> element by writing <book attr1="value1", attr2="value2"> .

In this article, we will keep things simple by working with elements without any attributes or values.

Where Are XML Files Used?

As we saw, XML provides a flexible and platform-independent way to represent structured data. Its human-readable format and ability to define custom elements make it suitable for various applications where data organization and interoperability are essential. Because of this, XML files are mainly used in two scenarios: configuration files and data interchange .

As a configuration file , XML is often used to define how a certain program should operate. This could be a web server, a program used by your own operating system, or a simple Python script that you wrote.

For example, an XML configuration file for a database could look like this:

The XML file above stores information related to database credentials, server settings, and logging functionality.

On the other hand, XML can also serve to interchange data between systems. If your Python script imports and exports data in XML, it becomes much easier to combine it with other systems that also use XML. It doesn’t matter what kind of database or storage your data lives in – as long as you know how to read from it and write back into it through XML. Because of this, XML is a popular format for data consumption in APIs.

As an example, the XML sample below could represent the result from a query in your company’s employee database:

Now that we've grasped the basics of XML, let's delve into how we can efficiently read XML files into Python for further analysis and processing.

How to Read XML Files in Python

We have a couple different options for libraries when working with XML in Python. In this section, we'll explore how to read XML files in Python using both built-in and third-party libraries. We’ll compare their approaches and functionalities.

In every example, we will use the books.xml file, whose content is laid out below:

Reading an XML File with Python's Built-In xml Module

Python's xml module provides functionalities to parse and manipulate XML data. Since this module is a part of Python’s standard library, there is no need to install anything besides Python itself.

The ElementTree class within this module offers a convenient way to navigate XML trees and extract relevant information. Take a look at the following example:

In the code above, we use ElementTree to parse the books.xml file. The ElementTree.getroot() method extracts the root element (i.e. the <bookstore> tag) into the root variable. Afterwards, we use the root.findall() method to find all <book> elements beneath the root element in the XML hierarchy.

We then iterate over each retrieved <book> element and get the corresponding content of its child elements <title> , <author> , and <price> with the book.find() method. Finally, we print the extracted information for each book in a formatted string. This displays the title, author, and price of each book.

Using BeautifulSoup to Read an XML File in Python

We can also use BeautifulSoup to read an XML file into Python. BeautifulSoup is a third-party Python library used to parse data stored as markup language. It is more commonly used to parse information in HTML files (particularly those obtained from web scraping), but you can use the library to parse content in XML files as well.

If you do not have BeautifulSoup in your Python environment, you need to install it using pip . You’ll also need to install lxml in order to make BeautifulSoup compatible with XML files. Execute the following command in your terminal to install both libraries at once:

The following examples does the exact same task as the previous one, but it replaces the xml module with BeautifulSoup :

As we can see, the basic structure of the code remains the same. The only differences are:

• We had to explicitly open the XML file with Python’s open()
• BeautifulSoup allows us to access a child element as if it were an attribute, i.e. we can write title.text instead of book.find("title").text .
• There are minor syntax differences, e.g. the method for retrieving all <book> elements is written find_all() instead of findall() .

Other than these small details, both approaches do exactly the same thing. Use whichever works best for you!

Reading an XML File into a Python Dictionary with xmltodict

As previously mentioned, XML files are structured similarly to Python dictionaries. While the syntax is different, the hierarchical structure and arbitrary tag names of an XML file is reminiscent of a Python dictionary.

In fact, we could combine Python dictionaries and lists to store the exact same data in books.xml :

This similarity begs the question: Could we parse the contents of an XML file directly into a Python dictionary? Luckily, the third-party library xmltodict does exactly that. To use it, we need to install it with pip :

Once we have it installed, all we need to do is to read the contents of the XML file as a Python string. We pass it to xmltodict.parse() so that it can perform its magic:

Thanks to the xmltodict module, we managed to read the entire data from the XML into a Python dictionary in just a few lines.

If you just need to extract a few bits of information – or if you need to perform a custom data processing pipeline when reading the XML file – you might be better off sticking to the xml or BeautifulSoup modules. But if all you need is a quick way to read the data in the XML file, the xmltodict module does the job just fine.

What’s Next with Reading XML Files into Python?

In this article, we went over how to read XML files into Python. We started by understanding what exactly an XML file is and where it is commonly used. We then explored how to read the XML file into Python using three different libraries: xml , BeautifulSoup , and xmltodict .

Being able to read XML files into Python opens up a world of possibilities for data analysts. Whether it's extracting or editing data from configuration files, parsing responses from a web service or API, or analyzing structured data, understanding how to handle XML in Python is a valuable skill for anyone working with data in Python.

To further your Python data analysis skills, we recommend you check out our courses on reading and writing CSV files , reading and writing JSON files , and reading and writing Excel files . Much like XML, these file formats are among some of the most used by any skilled data analyst. And remember that you get access to all of these courses and more in our Data Processing with Python Track !

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Harrison Butker Said His Benedictine College Commencement Speech Taken 'Out of Context'?

According to online posts, butker supposedly clarified in a statement, "all i said is that we should go back to a better time, like the 50s and 60s.", jordan liles, published may 16, 2024.

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On May 16, 2024, numerous users on Facebook , TikTok and X reposted a quote meme featuring a purported statement from Kansas City Chiefs kicker and 3-time Super Bowl champion Harrison Butker. The statement supposedly constituted Butker's response to some backlash following his May 11 commencement speech at Kansas' Benedictine College, a private Catholic liberal arts school.

In one post  on X displayed to over 1 million users, the viral quote meme showing a photo of Butker read, "Everyone is taking what I said out of context. All I said is that we should go back to a better time, like the 50s and 60s. When men were men, and women had more babies than thoughts. When the only 'Me too' movement was one woman saying she was ready for her 4th child, and another woman agreeing." The end of the meme added Butker's name with the words "on setting the record straight."

A TikTok video promoting the quote meme as genuine also received more than 800,000 views within five hours of being uploaded, making it another one of the more prominent reposts.

However, Butker did not release a statement with these words, nor did he appear to publicly release any statements following his speech. A closer look at the quote meme reveals a watermark for "@TheSportsMemery" — a reference to the Facebook page named The Sports Memery. The Facebook page's description describes its output as containing satire and parody.

The Associated Press reported Butker's speech featured some remarks on the subjects of women and motherhood, Pride month, in vitro fertilization (IVF) and President Joe Biden's policies regarding abortion and the COVID-19 pandemic, among others.

Readers looking to watch Butker's address in its original form can find the full, unedited speech in a  video  posted on the Benedictine College YouTube channel. The video ends with many of the people in attendance giving Butker a standing ovation.

The National Catholic Register also published a complete transcript of the address.

"Chiefs Kicker Butker Congratulates Women Graduates and Says Most Are More Excited about Motherhood." The Associated Press , 16 May 2024, https://apnews.com/article/kansas-city-chiefs-harrison-butker-e00f6ee45955c99ef1e809ec447239e0.

"Full Text: Harrison Butker of Kansas City Chiefs Graduation Speech." NCR , 16 May 2024, https://www.ncregister.com/news/harrison-butker-speech-at-benedictine.

"Harrison Butker | Commencement Address 2024 | Benedictine College." YouTube , Benedictine College, 11 May 2024, https://www.youtube.com/watch?v=-JS7RIKSaCc.

May 17, 2024: This report was updated to add the five words appearing under Butker's name in the quote meme.

By Jordan Liles

Jordan Liles is a Senior Reporter who has been with Snopes since 2016.

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Ebrahim Raisi death: What do we know about the Bell 212 helicopter?

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What are its uses, which organisations operate the helicopter, have there been other incidents involving the bell 212, iranian aviation, will there be an investigation.

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Reporting by Gerry Doyle, Additional reporting by Tim Hepher, Parisa Hafezi and Joanna Plucinska; Editing by Neil Fullick and Christina Fincher

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Shaken passengers arrive in Singapore after turbulence-hit flight

Many passengers on a Singapore Airlines flight hit by heavy turbulence which left dozens injured and one dead from a suspected heart attack finally reached Singapore on Wednesday morning.