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Text Mining: What you didn’t know

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text mining

Text mining involves using Machine Learning to analyse text. Find out everything you need to know: definition, how it works, techniques, benefits, use cases, etc.

Modern companies have a wealth of data on their customers or their industry. New digital technologies such as social networks, e-commerce, or mobile applications for smartphones provide access to a vast amount of information.

By analyzing this data, it is possible to discover untapped opportunities or urgent problems to solve. However, some types of data are more challenging to exploit than others.

Data from social networks or other websites consist primarily of texts: comments on posts, product reviews, complaints on community forums…

However, texts are part of what is called “unstructured data.” This information cannot be properly processed by traditional data analysis software and tools. Therefore, relying on “Text Mining” is necessary.

Text Mining, or text analysis, involves transforming unstructured text into structured data for subsequent analysis. This practice relies on “Natural Language Processing” (NLP) technology, enabling machines to understand and process human language automatically.

Artificial intelligence is now capable of automatically classifying texts by sentiment, subject, or intention. For example, a Text Mining algorithm can review product comments to determine if they are mainly positive, neutral, or negative. It is also possible to identify the most frequently used keywords.

As a result, companies can analyze large and complex datasets in a simple, fast, and efficient manner. This discipline also reduces time wasted on manual and repetitive tasks.

Teams save time and can focus on more critical tasks that require human intervention. Company leaders, on the other hand, can rely on data to make better decisions.


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How does text mining work?

Text Mining is based on Machine Learning, a subset of artificial intelligence that encompasses various techniques and tools that enable computers to learn to perform tasks autonomously.

Machine Learning models are trained using data to make accurate predictions. Text Mining automates text analysis using Machine Learning. To achieve this, algorithms are trained using text as example data.

The first step is data collection, which can come from internal sources like chat interactions, emails, surveys, or company databases. External sources such as social media, review websites, or news articles can also provide data.

Next, the data needs to be prepared using various Natural Language Processing techniques. This “data preprocessing” aims to clean and transform the data into a usable format.

This is a crucial aspect of Natural Language Processing, involving various techniques such as language identification, tokenization, part-of-speech tagging, chunking, and syntax analysis. These methods aim to format the data for analysis.

After completing this text preprocessing, the data is ready for analysis. Different Text Mining algorithms are then used to extract information from the data.

Analytical techniques

The “word frequency” technique involves identifying the most recurring terms or concepts in a dataset. This can be very useful, especially for analyzing customer reviews or social media conversations.

For example, if terms like “too expensive” or “overpriced” frequently appear, the analysis may suggest that the product is indeed too expensive, and adjustments to the price may be necessary if possible.

The collocation method, on the other hand, involves identifying sequences of words that frequently appear close to each other. Some words often appear together, forming bigrams or trigrams, which are combinations of two to three words. By identifying these collocations, it’s possible to better understand the semantic structure of a text and obtain more reliable Text Mining results.

The concordance method is used to recognize the context in which a set of words appears in a text. This technique helps avoid ambiguity and understand the meaning of a term in a specific context.

Information retrieval

Information retrieval, or IR, is the process of finding relevant information from a predefined set of queries or phrases. This approach is often used in library catalog systems or web search engines.

IR systems use various algorithms to track user behaviors and identify relevant data. Tokenization involves breaking down a long text into sentences or words called “tokens.” These tokens are then used in models for text clustering or tasks related to document association.

Stemming, on the other hand, involves separating word prefixes and suffixes to derive the root word and its meaning. This technique helps reduce the size of index files.

Text classification

There are also more advanced methods of Text Mining. Text classification involves assigning labels to unstructured text data. It is an essential and crucial step in Natural Language Processing (NLP).

Text classification helps organize and structure complex text to extract relevant data. Thanks to this technique, businesses can analyze various types of textual information to gain valuable insights.

There are different forms of text classification. Topic Analysis is used to understand the main themes or topics in a text. It is one of the primary ways to organize textual data.

Sentiment analysis involves analyzing the emotions conveyed in a text. This helps to better understand customer opinions, for example, by reviewing product reviews and classifying them as positive, negative, or neutral.

Language detection is used to classify text based on the language it is written in. For example, it can be used to sort customer service inquiries and direct them to an agent who speaks the appropriate language, saving valuable time.

Lastly, intent detection automatically recognizes the intentions behind a text. For example, analyzing various responses to a marketing email can determine which recipients are interested in a product.

Information extraction

Text extraction is another Text Mining technique that aims to extract specific data from a text, such as keywords, proper names, addresses, or emails. This helps avoid manual data sorting and saves time.

You can select the features that contribute the most to the results of a predictive analysis model, extract features to improve the accuracy of a classification task, or detect and categorize specific entities in a text.

Of course, it’s possible to combine text extraction and text classification, or other Text Mining methods, in the same analysis.

Text mining vs. text analytics: what's the difference?

Text Mining is often confused with Text Analytics. In reality, they are two slightly different concepts.

Both aim to automatically analyze texts but rely on different techniques. Text Mining identifies relevant information in a text, while Text Analytics aims to discover trends across large datasets.

One provides qualitative analyses, while the other provides quantitative analyses. Typically, Text Analytics is used to create tables, diagrams, graphs, or other visual reports.

Text Mining combines statistics, linguistics, and Machine Learning to predict results automatically from past experiences. On the other hand, Text Analytics involves creating data visualizations based on the results of Text Mining analyses. It is, of course, possible to combine these two approaches.

The benefits of text mining

Text Mining has numerous advantages, especially in an era where companies and individuals generate massive volumes of data every day. In fact, nearly 80% of text data is unstructured, making it impossible to analyze without Text Mining.

For instance, emails, social media posts, chat conversations, customer service inquiries, surveys, and more are challenging to manually sort through. Text analysis allows for the processing of large volumes of data within seconds, increasing productivity. These analyses can be conducted in real-time, enabling immediate intervention in case of any issues detected.

How can Text Mining be used?

Text Mining can be used in numerous ways by businesses. The applications of this technology are limitless and extend to all industries.

It enables the automation of text analysis, whether it’s for marketing, product development, sales, or customer service. Teams can gain efficiency and productivity by focusing on more critical tasks.

Customer service

In the field of Customer Service, for example, it’s possible to automatically sort customer inquiries. Text Mining automatically identifies the subjects, intent, complexity, and language of inquiries to organize them. Agents can then focus on providing assistance to customers.

If one inquiry is more critical or urgent than another, it can be automatically prioritized and addressed first. Additionally, text analysis also allows for measuring the effectiveness of customer service and user satisfaction.

Text Mining is also very useful for analyzing customer feedback and reviews about the brand and its products. This helps in understanding their opinions, expectations, and the quality of their experience with your company.

Product reviews, social media comments, survey responses can all be analyzed. This way, you can rely on data to make informed decisions and improve weaknesses.

Risk management

Text Mining is used in the field of risk management. It can be used to gather information about industry trends or financial markets by monitoring changes in sentiment or extracting information from analysis reports and whitepapers.

This can be particularly useful within banking institutions. The data allows for a more confident approach to investments in various sectors. Many banks are now adopting this approach.


In the field of healthcare, Text Mining techniques are increasingly used by researchers. Information clustering, for example, allows extracting information from medical books in an automated way.

This saves time and cost, making it a valuable resource for the field of medicine and healthcare.


Text analysis can also be particularly useful for cybersecurity. For example, it is possible to automatically detect and filter spam in email inboxes.

In this way, hackers can no longer use the spam method to hack computer systems. The risks of cyberattacks are drastically reduced, and the user experience is also improved.

How can I learn about text mining?

Textual data is becoming increasingly abundant, and text analysis is essential for data-driven companies in all sectors. To learn how to master Text Mining and its intricacies, you can turn to DataScientest’s training programs.

This discipline is part of our Data Analyst and Data Scientis course. These two courses will prepare you for careers as data analysts and data scientists, where Text Mining plays a central role.

All our training programs feature an innovative “Blended Learning” approach, combining in-person and online learning. You’ll benefit from the flexibility of online learning while staying engaged through in-person masterclasses.

These programs can be completed in just a few weeks as an intensive BootCamp format or over a few months as part of Continuing Education, allowing you to balance your studies with personal or professional commitments.

Upon completing these programs, learners receive a diploma certified by the University of Sorbonne. 90% of our learners find employment after completing the program. Don’t wait any longer; discover our training programs today.

You know all about Text Mining. Discover our complete article about AI Watermarking  and Convolutional Neural Networks.

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