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Text Analytics
Understanding Text Analytics
Case Study Example
Last Updated: Mar 27, 2024

Text Analytics and Big Data

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Ashwin Goyal
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In the real world, the data can be seen in two different categories. They are structured and unstructured data. Structured data are like table formats, text, etc. At the same time, Unstructured data includes images, audio, video, documents, emails, customer correspondence, tweets, and other formats. In these current technology developing days, the amount of variety of data is growing largely. Analyzing these types of data needs so many concepts and tools. Among them, one of the major types of data is text. Text is one of the common types of data that is used in our daily life. Understanding how to deal with this type of data is very important. To do this, many analytic techniques have been developed, called text analytical tools. We will learn more about text analytics in this article.

Text Analytics

Text is one of the old and most useful types of data. From the olden days itself, they are analyzing this text to achieve useful insights. But with developing technology, text usage has grown rapidly. The old/traditional methods are not working well on this current text type of data. Thus the development of text analytical techniques is raised. Nowadays, the industries are also trying to develop techniques to analyze the combination of both structured and unstructured data. 
The main objective of unstructured data is that the structure of data is unpredictable. The text is also present in different structures and formats from which software is created.
Forms of Text:

  • Documents
  • Emails
  • Log Files
  • Tweets
  • Facebook posts, and many more.

So, on seeing the above forms of text, we can state that the documents are more structured, the emails might have little structure, log files have their own structure, and similarly, Facebook posts and tweets have their own type of structure.
Understanding the formats and structure of text before applying analyzing techniques is very important for every analyst.

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Understanding Text Analytics

From the olden days, the development of various text analysis tools and concepts have been developed. But the major impact of developing these concepts is started only after the introduction of Natural Language Processing (NLP). NLP introduces so many terms and concepts that deal with text. This involves so many text analytical techniques. Text analytics is the process of analyzing unstructured text data and fetching so many valuable insights and transforming this data into structured data. After this, this transformed data can be used in various ways. This analysis and extraction involve various concepts, and techniques such as the use of computational linguistics, machine learning, neural networks, data mining algorithms, etc. 

Case Study Example

For example, if you are working in a Marketing department in a phone company, and you need to develop calling plans. Say you have two plans - Plan A and plan B. You are not getting much development through Plan A. You want to know the reason for this. 
The below-unstructured data may give you some insights.

Source: Big Data For Dummies - A Wiley Brand.

Here the underlined text may give you some useful information why your plan A is not working. For example, after analyzing the text, we got to know that the plan doesn’t include roll-over minutes, the plan was too expensive, the plan with 4GB of data is not enough, etc. From these, we can say that Ok! Fine, I think I need to add some changes in order to make this plan better. Hooray! We have made one analysis.
There is another possibility that we can convert unstructured data into structured data, and then apply some simple techniques to get our analysis done simply. For the above text, we can convert the text structured data from unstructured data and the results are shown below:

Source: Big Data for Dummies - A Wiley Brand

Now, on seeing this data we can simply say why our Plan A is not working properly.


  1. What is text analytics?
    Text analysis is the process of analyzing text and producing valuable insights, whereas text analytics involves the use of some concepts and techniques towards bringing textual content in a formatted form and then use this to get useful insights.
  2. How does text analytics work?
    Text analytics starts by breaking down the sentence and phrase into small parts. These include tokens, parts of speech, etc., which serve a lot and crucial role n the natural language processing. This process includes seven steps before deeper analysis.
  3. How do companies use text analytics tools?
    Companies use various text analytics tools and approaches to convert documents and online data into actionable insights. They majorly use text analytics tools for querying data such as keywords, etc.
  4. What are the different methods of text analytics?
    Text Analytics involves various techniques. Among them, currently, machine learning, deep learning, Natural language processing, and data mining techniques are more popular and useful for getting work done.
  5. How can I use text analytics to improve customer support?
    Customers always need the quality of a product. They need very concise and supportive solutions to their problems. Text analytics can be used to improve the context that is extracted from the sources and tune it by some text analytics methods.


I hope you have enjoyed the article so far. We have covered what unstructured data is, what are text analytics tools, how they impact our business and organizations, etc. We will try to explore this concept more in the upcoming articles.
You can also consider our Data Analytics Course to give your career an edge over others.
Happy Learning!

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