A textual analysis is a way in which someone can analyze and interpret written texts. Glosses, commentaries, and transcriptions are all forms of textual analysis that seek to explain the meaning of a text.
Text analysis can be broadly defined as any effort to study written language. It is applied in many fields to analyze both structured and unstructured data. The following are some of the common text analysis applications:
In a marketing setting, textual analysis is one of the most effective ways to interpret customer feedback. Most customers respond to surveys through written text responses, which marketers can easily analyze to identify common themes.
The feedback could be positive or negative. Text analysis can help marketers determine the best course of action to improve future products or services. These surveys help business people to analyze customer feedback and measure customer satisfaction.
An example of textual analysis in a marketing study:
“Market researchers conducted a textual analysis of hotel reviews from TripAdvisor. This study was to identify the qualities that motivate reviewers to write positive reviews.
The study found that guests write more positive and longer reviews when they: spend more, have a better experience, and receive better services.
In addition, guests write more positive reviews when they perceive that their friends and family would give the hotel a high rating. The views also depend on whether the hotel is committed to maintaining customer satisfaction.”
News outlets frequently use textual analysis to determine popular topics. For example, they might choose to cover a specific story based on the number of written responses they receive or the degree of interest their readers express in online polls.
An example of textual analysis in a news article:
“Newspapers used to count letters to the editor as a measure of public interest. It was assumed that the more letters a paper received, the more its readers cared about the topic. Letters to the editor were used as a proxy for public interest – at least until the rise of the internet.
“Now, online outlets can use textual analysis to determine public interest directly. They do so by monitoring the number of comments on an article about a specific topic.
If they find many readers are responding, the story may be deemed worthy of further coverage.”
Opinion polls are another marketing application that involves textual analysis. These polls commonly use written text – often in the form of open-ended questions – to gather data about consumer opinions. Some online surveys also ask customers to write their feedback through text.
“A recent study on the “Daily Show,” asked viewers about their viewing habits. They found that most viewers would prefer it if there were more episodes each week and fewer reruns. However, they also revealed that they watch it for free online, so advertisers need not worry.
Another study found college students are more likely to choose a movie theater with free refills and in-theater dining.”
One of the most common forms of text analysis is in natural language processing. The machine-learning algorithm uses textual data to uncover patterns, build models, and ultimately make predictions. NLP is often used in the context of sentiment analysis or language identification.
For instance, researchers are using natural language processing to predict the effectiveness of different medicines.
“A recent study of over 60,000 patients reveals that the effectiveness of antidepressants depends on how they are categorized. Researchers found three medication categories with significantly different outcomes: serotonin reuptake inhibitors, serotonin, and noradrenaline reuptake inhibitors, and monoamine oxidase inhibitors.
These medications are often prescribed to treat symptoms of depression. However, in some cases, they could be doing more harm than good.
Many patients are not aware that there are different antidepressants. Also, doctors do not always explain the differences between these medications.
It appears that a patient’s response to a prescription medication depends on how it has been categorized. In this case, text analysis is used to determine how the medication is categorized and why.”
Political speeches and talking points often follow a similar format, with each sentence building upon the previous. Political scientists can use textual analysis to identify political ideology and determine a politician’s point of view on current events. This analysis can be applied to identify common trends in political rhetoric and how it changes over time.
“In the past year, Presidential candidate Donald Trump has been criticized for his extreme views on immigration. The media has dubbed this view as racist because he wants to build a wall between the US and Mexico.
However, not all Americans agree with these views being labeled as racist. In fact, one study found that voters see the individual policy positions of Trump less as “racist” and more as “anti-immigration.”
Textual analysis, in this case, is used to determine whether or not some political views are being mislabeled as racist when they’re only “anti-immigration.”
In academic research where students handle a significant amount of data, textual analysis is used for various purposes such as:
- Introducing a new research methodology for textual data
- Extracting the findings from their own or others’ research
- Summarizing the findings of published papers.
An example of text analysis in research:
“Digitizing text has transformed information discovery, allowing researchers to examine vast amounts of texts for patterns. Using computational approaches, the study of literature has also undergone digitization.
However, the digitization of literature has sparked debate about whether computers can truly read and appreciate literature.”
Many companies use text mining software to find insights into textual data, including the IBM corporation.
“Last year, IBM’s Watson supercomputer defeated top-ranked Jeopardy! Champions Brad Rutter and Ken Jennings. This was the first time that a machine successfully defeated human contestants in a game of wits.
While Jeopardy! may be a game, it requires quite a lot of intelligence. The computer had to know the answers and understand language, and have a quick response time.
These insights can be used in marketing and to develop new products. For example, IBM found that by analyzing anonymized call center conversations. It could predict which customers would buy a product before they even started using the service.
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Textual analysis is very useful in historical documents and other texts which are written by hand. This process helps historians decipher the document’s original meaning and determine if it has been altered over time.
Family records and key documents such as wills and land titles can also be analyzed using textual analysis to determine the text’s authenticity.
“In 2012, a man in France who allegedly inherited a painting from his father was accused of forgery.
Investigators used textual analysis to check the authenticity of the painting. The language used in the will was not consistent with other known documents written by his father. Therefore, it is believed that the will was forged.
This analysis is commonly done before a trial begins to determine what evidence should be used in the trial.”
Most of us are familiar with an angry email that is unintentionally insulting to the recipient. However, it’s more difficult to pinpoint the tone of voice in written correspondence, though there are tools that can help.
Text analysis can also identify patterns in a tone of voice for both positive and negative sentiments. This is helpful in email correspondence and data science applications where the tone of voice can provide valuable context.
An example of tone analysis software:
“Some data scientists experimented on the sentiment in consumers’ energy bills. The scientists wanted to know if the sentiments could be analyzed from how the consumers sounded over the phone.
The company used its text-to-speech software, which is designed to mimic a real person’s voice. In the experiment, customers were asked to read their energy bills over the phone as they would to a human customer service representative.
The software then analyzed their tone of voice and determined that it was either positive or negative.”
In cultural studies, textual analysis is commonly used as a way to interpret and analyze written work. It is most often used in literature and historical writing. Cultural studies students use text analysis to study various topics such as social movements and government policies.
In humanities fields, textual analysis is one of the most common ways researchers interpret written texts.
“In Canada, the government is concerned that there are too many foreign workers in some industries. This could pose a threat to Canadian jobs and the economy in general.
The government is trying to determine if they should impose more strict quotas on temporary work visas. It also wants to monitor employers better to reduce abuse of the system.
To do so, the government officers are using textual analysis to analyze written work. For example, they studied over 100,000 work-related emails. This study was to determine if there was a strong correlation between the number of foreign workers and unemployment.”
Textual analysis is used in various contexts within the field of mass communication.
For example, media outlets frequently use textual analysis to determine the tone of voice in news stories.
Textual analysis is also used to determine if a news story should be labeled as an opinion piece, and if so, to what extent.
Media houses also use this analysis to determine which stories should be covered and which topics are of public interest.
“In 2012, the BBC was criticized for not featuring enough stories about women on its website.
The BBC is required by law to cover news stories that are of public interest. However, they were not doing so in the case of women’s issues.
Investigative journalists used textual analysis to determine which stories they covered should be considered public interest stories. They also determined which gender-related topics were being covered.
The results of this textual analysis showed that the BBC’s treatment of gender-related stories was not in line with the requirements.
Textual analysis is used in various stages of law enforcement, including investigations and arrests. This tool is helpful when the suspect’s written language is not easily available. Detectives can use the suspect’s writing style and other writings to uncover clues about their identity or potential motives.
Legal writing is very specific, with rules and guidelines that must be followed to the letter. This is because it is used as evidence in court cases. The language used by attorneys, prosecutors, and witnesses must be clear, concise, and unambiguous. Textual analysis is extremely important in the legal field to ensure that any written document can stand as evidence in court.
An example of how text analysis can be used in law enforcement:
“Detectives are investigating a string of robberies in the area. They believe that a different perpetrator is committing each robbery.
The police decide that they should use textual analysis to determine which suspects are responsible for the crimes.
They obtain samples of handwriting from several suspects. The handwriting samples are analyzed using a basic computerized text analyzer.
The results of this analysis show that two suspects’ writing matches the writing used in the notes that were passed to store clerks.”
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Genetic information is written in the language of DNA. Although the nucleotides are interpreted by other means, their arrangement follows rules that can be analyzed using text.
For example, if a pattern repeats itself in multiple samples, it could indicate a genetic mutation. Other health conditions could also be associated with that pattern.
Textual analysis is used to interpret genetic information in various ways, including identifying genetic mutations and genomic disorders.
Researchers often use genetic mutations to define a certain disease or create new drugs. However, these mutations are not always well-understood by the general public.
Genomic disorders are often caused by deletions in the genetic code that most people do not understand.
Researchers use textual analysis to decode this information and make it easier to understand for the average person.
“In one case, a researcher used textual analysis to decode a genomic disorder written in the language of DNA.
He looked for linguistic patterns in healthy people with one version of a gene and those with another version of the same gene.
Textual analysis of the patterns in healthy people with one version of a gene and those with another version helped him analyze. He could see how parts of the gene were lost when a mutation occurred. This was an important discovery that allowed him to create new treatments for this genetic disorder.”
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Direct marketing is one of the most common uses of text analysis in telecommunications. When companies send out promotional or transactional texts, they use textual analysis to identify their right recipients.
This analysis helps them personalize the text, increase response rates, and improve conversion rates for future texts. Custom communication gives a business a cutting-edge because their customers feel special.
“Hi, this is John from XYZ company. We are having a sale on our widgets. This week only, you can buy two widgets for the price of one! If you’d like, reply with ‘Yes’ to this message, and we will send you your special offer.”
In this text, the company used a computerized text analyzer to determine who would receive this message. The program analyzed the words and language used and matched them with a list of phone numbers. The numbers corresponded to people interested in the message and who might purchase as a result.
This analysis also determined that the message was transactional and not promotional. The latter would have made it more difficult to process any orders.
The language in task-oriented writing differs somewhat from other types of written text. It is usually much more concise and straight to the point. Because of this, the writing process is usually short and uses few words.
Deep learning algorithms are used to find patterns in task-oriented writing. For example, a monetary amount is always preceded by the word “dollar,” or the dollar sign ($) is used. In this case, the algorithm would identify that the monetary amount in dollars is always preceded by “dollar” or the dollar sign.
Other examples of task-oriented writing are purchase receipts, email signatures, or text messages between family members. Emails and text messages sometimes go through the same analysis, especially if they’re going out to the same people.
For example: “Hi, I’m Joe, my address is 123 Main Street, Anytown USA 01234. My sister’s number is 555-555-5555. I’m having a party on September 20th, at 7 pm. Please RSVP to this email or text me if you’re coming.”
This information could be used to personalize a text message or email to show what was included in the previous texts.
Social media posts are informal and can include slang or other nonstandard languages, so it’s important to analyze them using the right tools. Social media posts analysis can help brands to identify trends and other insights about their customers.
“A social media user posts a picture of a TV commercial on Instagram. Other users respond with their opinions about the ad in the comments section.
The brand uses social media posts analysis to see what users are saying about the TV ad and what features or products are of interest. This analysis helps the brand to make informed decisions about future TV ads.”
Also, if social media users use slang words like “LOL,” “YOLO,” and “OMG,” it would be hard to make sense of the posts.
Textual analysis will decode:
|• LOL-Laughing Out Loud|
• YOLO-You Only Live Once
• OMG-Oh My God
For all these reasons, it is important to use the right tools for social media posts analysis.
With textual analysis via machine learning, companies can detect customers’ intents.
For example, if someone searches for “engagement rings near me,” they want to purchase rings, not diamonds. The task will be interpreted in a different way than someone searching for “engagement diamonds near me.”
The algorithm would detect the intent behind a search and deliver results specific to that task.
In business, this means improving sales from customers who will spend more than other potential customers.
Textual analysis is used in both consumer and business applications for various tasks. The three main examples are:
3. Task-oriented writing
All three uses of textual analysis include the need for machine learning.
Additionally, all three types of tasks are used to generate more sales and revenue in the business world. Because this is the case, analyzing the language of business is important for many companies.
There are various common methods used to perform textual analysis, and the process can vary based on the type of data and the objective. The most common types include:
This method of textual analysis is used to count words, calculate frequencies, and compare them against other documents. These results are used to identify writing style, measure productivity, and more.
Data visualization is a method of textual analysis that uses charts, graphs, and other visual representations to find themes in data. These include word clouds, heat maps, and tag clouds.
It is a common type of data visualization and textual analysis. This process involves taking the words from a text and calculating their relative weights based on the frequency of use. These words are formatted as a tag cloud where the fonts represent those weights.
A computer program will extract a paragraph from a text and then summarize or analyze it in this form of textual analysis. This is one of the most straightforward methods for extracting text.
It is a specific type of topic modeling that uses words in context to identify which sense of the word the author intended. This method is particularly helpful when analyzing something like a legal contract that uses specific terminology.
Word-sense disambiguation doesn’t identify the word’s meaning, only which sense (i.e., definition) is intended for that context.
This method is used to perform general-purpose analysis on a common text. It uses complex algorithms to extract the meaning from a text. Statistical NLP can be helpful when statistical information in the text needs to be determined.
An example application of textual analysis is sentiment analysis. This analysis categorizes written work according to positive and negative sentiments.
The quantitative content analysis breaks down textual data into quantitative results to identify trends and patterns. This type of text analysis is often used in content marketing to identify the most common topics in a body of work.
An important part of textual analysis is ensuring that the tools used are accurate and efficient. This will require a good understanding of the strengths and limitations of the tools and how to use them. Below are the steps followed in textual analysis:
This first step involves getting the text into a format that can be stored and analyzed. This often involves removing punctuation, abbreviations, or other symbols that may interfere with the process.
Data preparation will also include tokenizing the work into individual words. It is necessary to identify word-sense disambiguation and reduce lexical analysis errors.
This process involves further cleaning the text to address any shortcomings or errors. The most common application of this step is correcting spelling and grammar mistakes. It can also be used in conjunction with other text-based preprocessing tasks such as word stemming.
It is the reduction of the text into a form that is easier to analyze by converting it into numbers. This step might involve breaking phrases down into individual words or identifying the parts of speech for each word.
Computational analysis means running the text through algorithms to extract the information that will be used for analysis. During this step, files are often converted into vectors or matrices so that data mining and machine learning algorithms can be used.
This step involves looking through the computational analysis results to identify trends, patterns, or other useful information. The things that researchers look for in these texts are called themes. They can also interesting insights defined by either a unique characteristic or an unexpected connection between two items.
- The biggest downside of textual analysis is that it’s not always possible to draw firm conclusions from statistical information. It can be difficult to take into account all of the variables involved.
- This form of analysis is also time-consuming. It requires researchers to spend many hours on data collection and preparation before beginning an analysis. This makes it difficult to scale up the process for research on a large scale.
- Textual analysis is also language-dependent. Different languages have different rules, so it’s important to use the right tools for each language.
- The training data used in some of the methodologies can be inaccurate.
While textual analysis is not perfect, it can be a useful tool for marketing and other areas. The best way to get accurate results is to find the right tools for your specific type of work and use them with other analytics.
By using textual analysis, researchers can quickly and easily compile research from a variety of sources. In this form of analysis, researchers look for patterns in the literature to identify new trends and insights. In this way, textual analysis can be used to analyze written work in various fields, including literature and cultural studies.