Text Mining

Text mining is the process of discovering and extracting useful information and patterns from unstructured natural-language text, turning that text into structured data that can be analyzed. It combines natural language processing (NLP), statistics and machine learning. It is, in essence, the application of data mining to the territory of free text: emails, documents, reviews, social media posts.

Preprocessing

Raw text cannot be analyzed as is, so it is first prepared. The usual steps are tokenization (splitting the text into units, usually words), the removal of stopwords (such as “the,” “of,” “and,” which add little), lemmatization or stemming (reducing words to their base form) and numerical representation, via the bag of words, TF-IDF weighting or embeddings.

Its tasks

On this basis, text mining tackles several tasks. Text classification assigns categories to documents; named entity recognition locates people, places or organizations; sentiment analysis detects the emotional charge of a text; topic modeling discovers its themes; and automatic summarization condenses it.

What it is for

Its applications are broad: the analysis of customer opinions, business intelligence, spam detection and, very prominently, the mining of biomedical literature, which combs through millions of scientific articles in search of relationships between genes, drugs and diseases.

The arrival of large models

In 2026, text mining has been transformed by large language models (LLMs). Unlike earlier methods, which required hand-crafted rules or a model trained for each domain, LLMs extract information with a remarkable capacity for generalization and text comprehension, often without specific training. In exchange, they raise challenges of computational cost and data privacy.