Table of ContentsApproaches to Text MiningUsing Proven Methods and Understanding Text Mining ResultsBlack Box Approaches to Text Mining and Extraction of conceptsSkepticism is required when using such algorithms, because when executing their On a daily basis, organizations are confronted with textual data. The data source may be electronic texts, call center logs, social media, corporate documents, research papers, application forms, memos, emails, etc. These data may be accessible but remain unexploited due to lack of awareness of the information. the wealth that an organization has or the lack of methodology or technology to analyze this data and obtain useful insights. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayThe goal of Text Mining is to process unstructured (textual) information, extract meaningful numerical clues from the text and, thus, to make the information contained in the text accessible to the different data mining algorithms (statistics and machine learning). Information may be extracted to derive summaries of the words contained in the documents or to calculate summaries of the documents based on the words contained therein. Therefore, you can analyze words, groups of words used in documents, etc., or you can analyze documents and determine the similarities between them or how they relate to other variables of interest in the data mining project. More generally, text mining will “transform the text into numbers” (meaningful clues), which can then be incorporated into other analyzes such as predictive data mining projects, the application of unsupervised learning methods (clustering), etc. analyze, text mining is the discovery of knowledge from textual data or the mining of textual data to discover useful but hidden information. However, many people have defined text mining slightly differently. Here are some definitions: “The goal of Text Mining is to exploit information contained in textual documents in a variety of ways, including…discovery of patterns and trends in data, associations between entities, predictive rules, etc. . » (Grobelnik et al., 2001). “Another way to look at text data mining is to think of it as a process of exploratory data analysis that leads to previously unknown information, or to answers to questions for which the answer is not known. currently known. » (Hearst, 1999). Text mining, also known as text data mining or text analysis, is the process of discovering high-quality information from text data sources. The application of text mining techniques to solve specific business problems is called business text analysis or simply text analytics. Text mining techniques can help organizations derive valuable business insights from the wealth of textual information they possess. Text mining transforms text data into a structured format through the use of several techniques. This involves identifying and collecting text data sources, NLP techniques such as voice tagging and parsing, entity/concept extraction which identifies named features.