Text Analysis: An Overview Guide On Concept, Techniques & Use Cases

11 min readNov 25, 2021
article that gives the overview of text analysis technique

The advent of data has been tremendous in recent years, all thanks to the rising influence of technology in our daily lives. Data can be in multiple forms. Text data is one of them and is available in the forms of survey responses, emails, social media posts, customer queries or complaints, and much more. Doing text analysis can benefit organizations such as e-commerce and product-oriented companies by helping them understand the sentiments of their customers.

If you are working for an organization that uses data extensively, there are good chances you might already be analyzing it.

However, if you are looking for better ways to analyze text data at scale, we have a better option. Yes, we are talking about advanced techniques such as machine learning to mine insights from texts. This article looks at the intricacies of text analysis and how you can use it effectively for your business.

What is Text Analysis?

In the ever-changing world of technology, data analysis has become a vital component of success. One way of doing that is through text mining or content analysis. The process involves extracting valuable information from human language intelligently and efficiently through the natural processing power of computers.

The process works with unorganized pieces where each document gets disintegrated into its different parts. It allows easy management as every bit becomes critical no matter how small they might be. There are two ways to analyze text. The first is qualitative and includes details such as word choice and sentence structure.

The second type of analysis takes these features into account. It also looks at how often certain words appear compared to others within documents or graphics files for quantitative data.

Users can then use them when writing conclusions based on the things discovered during research. Developers and researchers use text analysis for various tasks, including summarizing information between two entities automatically.

A researcher using this technology can generate summaries in any language they wish with their voice or by typing out phrases on keyboards. It becomes easier to view data quickly rather than entering everything manually into Excel spreadsheets.

Businesses also get a whole new set of opportunities by slicing textual sources into easy-to-automate data pieces. With text analysis, they can use these slices to make decisions and optimize marketing campaigns for better results in no time. Besides, text analysis is also ideal for content management, content recommendation, and semantic search.

Text Analysis vs. Text Mining vs. Text Analytics

People usually use text analysis and text mining interchangeably. They are also more like synonyms of each other. Text analysis describes the process of computational analysis of texts. Whereas, text analytics is the method you adopt to showcase textual content as data. You can then mine or analyze it to extract relevant insights.

These three terms are closely related to Natural Language Processing (NLP). The technique involves extracting the necessary information from texts logically. Users need to manage computational cost, analytical efforts, and accuracy to get the best output. Let’s consider an example here.

“India is one of the most populated countries in the world. However, India still ranks behind China in terms of the global population count.”

In the above sentence, text analysis helps computers understand that the text is about India, China, and their population. The application of text analytics to this sentence shows that the mention of India and China is about their people and not as a tourist spot.

Text Analysis vs. Natural Language Processing (NLP)?

When we talk of text analysis, natural language processing is one of its sub-domains. It thus becomes difficult to differentiate between the two. As we have already seen, text analysis involves exploring large datasets to extract meaningful insights. NLP is a tool used to train machines and make them read and understand human speech.

It is also one of the methods for extracting insights in the process of text analysis. You can also relate NLP with AI, ML, and computational linguistics. Text analysis relies on the text and words while not considering the semantics. You can use it to check the presence of their words, their frequency, and sentence length.

NLP goes deep and understands the context and linguistic use of texts. It also analyses semantics and grammar. Emails are a good example here, as NLP understands the intention of the text and accordingly sends them into folders like primary, social, etc.

Text Analysis Methods & Techniques

Let’s look at the various techniques involved in the process of text analysis.

Text Classification

It involves unstructured text and assigning it categories and tags. The process has several benefits, making it one of the popular natural language processing techniques. You can use it to categorize and structure text to identify meaningful insights. As an ML technique, it can complete the analysis of text much faster than humans.

Word Frequency

It involves counting the words that appear the most in a particular text document with the help of numerical statistic TF-IDF. It is ideal in situations where you want to identify terms used most by your customers. If the word ‘customer support’ frequently appears negatively in your product reviews section, it might point to a related issue.

Text Extraction

Text extraction is an analytical technique that extracts data from text, such as words or phrases. It can extract keywords and product specifications in conjunction with other analyses like sentiment analysis and customer for categorization purposes.


You can use this technique to figure out the context of the given text. It is also ideal for checking the instances of words. If you have a large chunk of customer reviews related to your product, you can use this technique to identify words and in what context they got used. For example, the word ‘average’ can clarify whether the product or customer support was ordinary.


Collocation is the study of word pairs that occur together frequently. There are several bigrams (two adjacent words) and trigrams (three adjacent words) we use in our daily lives. For example, words like “good customer support” is an example of the trigram.

Word sense disambiguation

The problem of word sense disambiguation is a challenge for natural language processing. Take the example of “bat,” which can refer to either a cricket bat or animal. However, when you train models, they will be able to differentiate between these meanings.


Text clustering is a method of organizing and understanding unstructured data. It is not as accurate as classification algorithms, but this process can be quicker. There is no need for you to tag examples to train models. Instead, the process relies on an algorithm that mines information from text without any previous input.

Industry-wide Use Cases of Text Analysis

Here are the various use cases of text analysis in different industries.

Pharma Industry

  • Anonymizing the patient information in clinical trials (Named Entity Recognition): Gramener has helped a global healthcare organization to anonymize public healthcare information through an automated solution. Clinical trial documents are vast and come with a risk of exposing the private information of the participants. It thus becomes imperative for clinical research organizations to clean the data manually. Named Entity Recognition eases this difficulty through an automated process that completes in just a few hours.
  • Evaluating medical journals for drug discovery (pharma literature mining): Unstructured text is an essential component in deciphering genotype and phenotype knowledge found in biomedical publications. These publications provide critical information for interpreting genetic data. Clinical notes also contain rich sources of EHR-based knowledge. It includes medical terminology or observations about patient behavior. These might not get captured by structured fields on their record. Text mining can transform these texts and make them accessible to machines, so they’re ready when you need them most. You can use it to extract relevant bits for analysis or generate hypotheses from the information.

E-commerce and Retail

  • Voice of customer analysis: Customer analytics solutions are becoming popular as they provide an accurate way to understand customer satisfaction with your company’s products or services. With the help of customer sentiment analysis, you can track how customers feel about different aspects of their journey through various touchpoints. It will help you improve areas where there may be room for improvement based on customer feedback.

At Gramener, our NPS Analytics solutions are custom-built on low code technology. Customer Journey Identification will assist companies in prioritizing activities based on customer rankings and comments.

Here’s an NPS Analytics solution architecture with a Machine Learning model that accurately determines NPS score using Sentiment & Satisfaction Analysis with an accuracy of 84 percent. The Client Analytics solution from Gramener may assist you in identifying elements that influence customer intimacy and curating compelling customer experiences throughout the journey.

machine learning model to analyze customer feedback text
  • Product analysis: The voice of the customer is crucial in today’s market. Businesses should be listening to what customers are saying and using that information for their benefit. They can develop robust roadmaps that provide consumers with exactly what they want without guessing or relying on assumptions. Sentiment analysis helps you understand the customer dislikes about your product. You can also compare your reviews with those of competitors. There are also insights in real-time that save hours of manual work in understanding your customer psyche.

Legal Industry

The time spent reviewing legal documents is a waste of resources, especially for busy professionals. The process becomes cumbersome when the number and type are abundant in the categories they contain. It becomes hard to assess risks and determine which ones carry with them increased consequences.

AI-enabled contract risk identifier classifies documents into clause-based categories. Lawyers do not have to spend hours looking through each clause while still getting instant feedback about their findings.

text analyzer for legal documents

Media Content Analysis

Media content analysis is the use of quantitative or qualitative research methods to analyze pieces of media. You can understand your profile by evaluating issues, messages delivered in coverage, advocates for specific points of view, and critics who offer negative feedback. You can do it through ratings given in either print, broadcast, or online media.

Finance Industry

The demand for natural language processing in banking is increasing thanks to the ability of text mining techniques to gauge customer sentiment, enterprise search, and more. Banks use AI systems to navigate vast data filled with information about customers and internally produced documents containing compliance requirements. It helps them gain valuable business insights within no time.

Insurance Industry

  • Fraud detection: Text mining involves analyzing patterns and assertions from data, including insurance applications. It helps in generating knowledge databases that investigators use to detect fraudulent cases. Investigators do this by identifying keywords or descriptions related to incidences across locations with multiple claimants involved. It can be a red flag for organized fraud as well.
  • Understanding consumer pain points and needs: Reviews are excellent for consumers to share their thoughts on the insurance products they are buying. From a company’s perspective, this feedback is an essential tool as it provides insight into the likes and dislikes of people. However, monitoring all these comments could take up too much time. It is where AI and text analysis come into the picture.
  • Claims management and analysis: By automating complaints and claims, text mining can accurately divide them into their categories. Automated classification ensures that they get directed towards the concerned person. It helps save time and improves the response time, leading to improved customer satisfaction.
  • Insurance call center notes analysis: Text analysis enables organizations to analyze the customer experience even over phone calls. A call center representative can research to find out if this person had experienced lousy service in the past or was a loyal follower. If the customer is from an important category, the call center representative can ensure a pleasant experience.

Importance of Text Analysis?

Text analysis is an ideal solution for businesses looking to improve their business processes. You can quickly get insight into how customers perceive your products and services by analyzing feedback in an instant with these powerful resources. It helps employees save time from manually analyzing one review at a time.

If there are hundreds of reviews, the time required will be much more. If there is a surge in sales, it can further complicate the job. Text analysis is scalable and helps you analyze vast datasets in minutes. Your resources can instead spend their time on other meaningful work.

Analyzing Text Data

Let’s look at the process of analyzing text data.

Data Gathering

There are two types of data you can gather data for your brand and its product and services. It includes internal data and external data. Let’s look at internal data gathering first.

1. Internal data

It involves your day-to-day data like customer surveys, emails, chats, queries, and complaints. You can export the data from your software in an Excel or CSV file and connect it to an API for retrieving. Some of the examples of internal data are:

  • Customer service software: It is the software that you use for communication with your customers. It includes managing their queries and addressing issues.
  • CRM: It tracks the interactions with your prospects and regular customers. Data generated from various teams like sales, marketing, and customer support will be available here.
  • Chat: It involves applications that you use to chat with your customers and even the internal team members.
  • Email: It involves all the formal business-related communication in writing you have with your customers.
  • Surveys: It includes the feedback you gather from your customers about the performance of your product or service.
  • NPS (Net Promoter Score): It is the data you get by measuring the customer experience and satisfaction levels of using your product.
  • Databases: It is the collection of information of a particular type. You can manage and analyze data using a related database management system.
  • Product analytics: It involves the data about the interaction between you and your customers through various means.

2. External data

It is the data related to your product or service, which you can gather from across the web. Some of the examples of external data are:

  • Web scraping tools: You can use visual web scraping tools and web scraping frameworks. It is easy to create a web scraper without coding experience.
  • APIs: Social media platforms like Facebook and Twitter allow you to extract data through APIs. You can use that to pull relevant information related to your business.
  • Integrations: You can use integrations to connect to platforms like Twitter, Gmail, Google Spreadsheets, and extract data.

Data Preparation

Data organization is critical if you want to analyze textual information through machine learning algorithms. You can automate the process, and it will get completed in minutes. Here are some techniques through which you can do automatic text analysis.

  • Tokenization, Part-of-speech Tagging, and Parsing
  • Dependency Parsing
  • Lemmatization and Stemming
  • Stopword Removal

Pros of Text Analysis

  • Helps you understand the root cause of issues.
  • Enables you to predict emerging trends that you can miss through surveys.
  • Allows you to prioritize issues quickly and efficiently.
  • Gives you the chance to implement feedback, leading to improved customer satisfaction.

Cons of Text Analysis

  • Wrong interpretation of the analyzed text can lead to undesired outcomes.
  • Results can be subjective at times, leading to ambiguity in decision-making.


Text analytics is a powerful tool today that helps businesses gain actionable insights from their text data. It saves them time, automates tasks, and increases productivity.

Organizations can offload cumbersome work from their teams and allow them to work on meaningful tasks. It is a win-win situation for customers as they get professional service.

At Gramener, we help solve the data analysis challenges for businesses with our range of proprietary solutions built on the Gramex low-code platform. Get in touch with us today to know more about it.




Gramener is a design-led data science company that solves complex business problems with compelling data stories using insights and a low-code platform, Gramex.