Text mining, also known as text data mining, equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically obtained through the design of patterns and trends through means such as the learning of statistical patterns. Text mining generally involves the process of structuring the input text (generally analysis, along with adding some derived linguistic features and removing others, and then inserting them into a database), deriving patterns within structured data and, finally, evaluation and interpretation. of departure. “High quality” in text mining generally refers to a combination of relevance, novelty and interesting. Typical text mining tasks include categorizing text, grouping text, extracting concepts / entities, producing granular taxonomies, analyzing sentiments, summarizing documents, and modeling relationships between entities (i.e. , the learning relationships between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, labeling / annotation, information extraction, data extraction techniques, including link analysis and associations, visualization and predictive analytics. The overall goal is essentially to convert text to data for analysis, through the application of Natural Language Processing (NLP) and analytical methods. A typical application is to scan a set of documents written in natural language and model the set of documents for predictive classification purposes, or fill a database or search index with the extracted information.

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