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What is Text Analysis?

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Do you know precisely what unstructured data is? If not, don’t worry; keeping track of all the terms and buzzwords associated with digital information is challenging. Unstructured data in online images, audio, video, or social media posts and conversations surround us daily. And let’s add e-mails, business documents, and webpages to that pile. So, how should a data analyst extract meaningful insights from that mess? Why, with text analysis, of course!

This article answers the question, “What is text analysis?” We’ll define the term, show how it works, why it’s important, and examine its techniques and applications. We’ll also share a data analytics program to help professionals upskill.

Let’s get down to business by defining the term. What is text analysis?

What is Text Analysis?

Text analysis (also called text mining and content analysis) is a machine learning technique used by computers to efficiently and intelligently extract valuable information from unstructured data. Developers and researchers use text analysis to convert diverse and unorganized data into a structured form. During this process, documents are disintegrated for trouble-free data piece management. To put it in simpler terms, text analysis converts unstructured text into structured data.

Now that we’ve clarified text analysis, what about text analytics? Are they the same? If not, then what is text analytics, then? It’s time for a comparison.

Also Read: Tutorial: Data Analysis in Excel

Text Mining vs. Text Analysis vs. Text Analytics: A Comparison

We have already established that text mining is just another term for text analysis. People often use the terms interchangeably to describe the same process of gathering data via statistical pattern learning.

So, what’s the difference between text analytics and text analysis?

The short version: text analysis provides qualitative results, while text analytics delivers quantitative results. If a computer performs text analysis, it’s identifying valuable information from the text itself. Still, if it’s performing text analytics, the machine discovers patterns across vast amounts of text, producing graphs, reports, and tables.

For instance, an IT help desk manager wants to know how many support tickets each team member has resolved. In this case, the manager uses text analytics to create a graph displaying individual ticket resolution rates.

However, if the IT manager also wants to know the proportion of tickets with positive or negative outcomes, they need text analytics. The process analyzes the text within each ticket and subsequent exchanges so the IT helpdesk manager can see how each agent handled their tickets and if customers were satisfied with the outcome.

So, text analysis’s main challenge is decoding the ambiguity of human speech, while text analytics’ big challenge is detecting trends and patterns from numerical results.

How Does Text Analysis Work?

How do we get text analysis to work? As with many artificial intelligence, machine learning, and natural language processing functions, the answer is algorithms. So, suppose a data analyst wants text analysis software to perform a particular task. In that case, they need to teach machine learning algorithms how to analyze, understand, and pull meanings from the text. But how? They accomplish this by tagging text examples. Once the machine has enough tagged text examples, algorithms can start differentiating and making associations between bits of text and create predictions.

The Importance of Text Analysis

Organizations use text analysis to prepare for employing a data-driven approach to content management. Once textual sources are broken down into easy-to-automate data pieces, new opportunity processes, such as marketing optimization, decision-making, business intelligence, product development, and more, are opened.

Capturing data through text analysis supports tasks like:

  • Content management
  • Semantic searches
  • Content recommendations
  • Regulatory compliance

Textual sources are turned into actionable data that can also be used to extract valuable information, use and reuse content, discover patterns, automatically manage, search beyond keywords, and much more.

Using text analysis is one of the initial steps for many data-driven approaches since the process pulls machine-readable facts from large bodies of texts and lets these facts be automatically entered into a database or a spreadsheet, which are then used to analyze data for trends, provide a natural language summary, or used for indexing purposes in information retrieval applications.

Also Read: Overview: What is Exploratory Data Analysis?

Common Text Analysis Techniques

Let’s study some of the more common text analysis techniques.

Text Classification

Text classification assigns predefined categories or tags to unstructured text. This technique is one of the most beneficial natural language processing (NLP) techniques because of its versatility. It can organize, structure, and categorize any text to provide meaningful data and solve problems. Text classification tasks include:

  • Sentiment analysis, where customers leave their opinions on goods and services via interactions such as surveys.
  • Topic analysis, which automatically organizes text by theme or subject.
  • Intent detection, where machine learning is used to detect the intent of the text.

Text Extraction

Text extraction pulls pre-existing pieces of data within any text, extracting keywords, company names, prices, and product specifications from product reviews, news reports, and other sources. This technique is broken down further into:

  • Keyword extraction is used to index the data to be searched and generate word clouds.
  • Entity recognition finds entities (e.g., companies, people, or locations) within the text data.

Word Frequency

Using a numerical statistic, word frequency measures the most frequently occurring concepts or words in a particular text.


Collocation finds words that commonly occur together, such as “customer service” or “maintenance plan.”


Concordance identifies the context and instances of words or groups of words. For example, a concordance of the word “purchase” can help marketers understand how customers/users are using the word.

Word Sense Disambiguation

Many words have more than one meaning. For instance, “iron” is an appliance, a metal, or a verb. Smart text analysis trained in word sense disambiguation can differentiate between the meanings.


Text clusters can understand and group large amounts of unstructured data. Although clustering is less accurate than normal classification algorithms, it is faster to implement since the analyst doesn’t need to tag examples to train models. So, smart algorithms mine information and make predictions without using training data, a process called unsupervised machine learning.

How to Analyze Text Data

Here’s a series of easy steps to take when analyzing text data.

  1. Gather your data. Pull your data from internal sources (chats, e-mails, employee surveys, invoices) and external sources (online reviews, social media posts, news articles).
  2. Prepare your data. Next, you must structure your raw data into a format more conducive to analysis. Use the following natural language processing methods:
    • Tokenization. Tokenization segregates raw text into many parts that make semantic sense. For example, the phrase text analytics improves digital marketing tokenizes the words text, analytics, digital, improves, and marketing.
    • Part-of-speech tagging. Part-of-speech tagging attaches grammatical tags to tokenized text. For example, applying this step to the previously mentioned tokens results in text: Noun; analytics: Noun; improves: Verb; marketing: Noun.
    • Parsing. Parsing uses English grammar to establish meaningful connections between tokenized words with English grammar.
    • Lemmatization. Lemmatization simplifies words into their dictionary form, known as lemma. For example, the dictionary form of monetizing is monetize.
    • Stop words removal. Stop words provide little or no semantic context to a sentence. These are words like as and, or, and for. The software often removes them from the structured text.
  3. Conduct text analysis. Text analysis is the fundamental part of this process, where text analysis software processes the text by:
    • Text classification. Classification assigns tags to the text data based on rules or machine learning-based systems.
    • Text extraction. Extraction identifies specific keywords in the text and associates them with tags. Text analysis software uses conditional random fields (CRFs) and regular expressions.
  4. Visualization. Visualization turns text analysis results into easily understandable formats, such as graphs, charts, and tables. These visualized results help marketers to identify trends and patterns and create action plans. Many text analysts use tools such as Tableau, Google Data Studio, or Looker.

Also Read: Data Analyst Job Description: What Aspiring Professionals Need to Know

Five Text Analysis Examples and Applications

Here are five examples of how text analysis is applied to today’s IT-driven world.

  1. Preventing cybercrime. The Internet is a highly vulnerable medium for communication and data sharing. Text analysis is most likely one of the very few techniques successfully used to fight cybercrimes.
  2. Efficient customer service. Excellent customer service is one of the fundamental examples of how text analysis caters to improving customer service through mediums such as survey software or customer satisfaction follow-up, resulting in better products or services. This technique builds customer trust by offering fast, automated responses to customers when they need help.
  3. Advertising through digital media. Thanks to today’s increasingly digital world, advertising firms rely more on digital mediums to collect reliable results. Text analysis is one of the main tools advertising firms use to gather precise 360-degree results.
  4. Content enhancement. Humans generate content, but content enhancement eases the process by managing the sizeable bulk of data. Through text analysis, content can be enhanced by adding multiple aspects like organizing or providing the content with an outline so it can be applied to multiple implementations.
  5. Social media network data analysis. Social media is the most effective medium for connecting with your target audience and getting feedback, reviews, and criticism. This interaction can be used to improve goods and services and gives the company access to a helpful pool of data. Companies use social media strategies to gather insights into their products’ performances and understand the typical buyer’s persona. These insights help the company make the right improvements. Text analysis simplifies implementing vast amounts of data, extracting results from the analysis, and understanding user feedback and moods.

Do You Want to Develop Data Analytics Skills?

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Data analysts can earn an average of over $77K per year, according to So, if you’re looking into a career change, consider data analysis and this highly instructive online bootcamp.


Q: What is meant by text analysis?
A: Text analysis is a machine learning technique computers use to extract valuable information from unstructured data efficiently and intelligently.

Q: What is an example of text analysis?
A: A company can use brand monitoring to monitor real-time comments and track positive and negative feedback on its products. By using text analysis to analyze customer reviews, especially for certain words or phrases, the company can better understand customer sentiment and improve its offerings.

Q: What are the functions of text analysis?
A: Text analysis functions are typically broken down into topical text classification, word frequency analysis, emotion or sentiment analysis, visualizations, and data management.

Q: Why is text analysis useful?
A: Text analysis lets businesses quickly and efficiently structure large quantities of information, such as e-mails, social media, chats, support tickets, and documents. This process provides real-time feedback, allowing the business to make faster product changes and allows organizations to redirect internal resources to more urgent tasks.

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