The article provides an overview of text mining, a field that aims to extract useful patterns and knowledge from unstructured text data. Text mining is interdisciplinary, involving information retrieval, machine learning, statistics, computational linguistics, and data mining. The main analysis tasks discussed include preprocessing, classification, clustering, information extraction, and visualization. Preprocessing involves converting text into a suitable format for analysis, such as bag-of-words representation. Classification methods like Naive Bayes, k-nearest neighbors, decision trees, and support vector machines are used to categorize documents. Clustering algorithms group similar documents together, and evaluation measures like silhouette coefficients and purity are used to assess the quality of clustering results. The article also highlights successful applications of text mining in various domains.The article provides an overview of text mining, a field that aims to extract useful patterns and knowledge from unstructured text data. Text mining is interdisciplinary, involving information retrieval, machine learning, statistics, computational linguistics, and data mining. The main analysis tasks discussed include preprocessing, classification, clustering, information extraction, and visualization. Preprocessing involves converting text into a suitable format for analysis, such as bag-of-words representation. Classification methods like Naive Bayes, k-nearest neighbors, decision trees, and support vector machines are used to categorize documents. Clustering algorithms group similar documents together, and evaluation measures like silhouette coefficients and purity are used to assess the quality of clustering results. The article also highlights successful applications of text mining in various domains.