This article provides an overview of text mining, a field that combines information retrieval, machine learning, statistics, computational linguistics, and data mining to extract useful information from unstructured text. Text mining involves preprocessing, classification, clustering, information extraction, and visualization to identify patterns and knowledge from text. It is closely related to knowledge discovery in databases (KDD), which focuses on finding valid, novel, and useful patterns in data. Text mining is often used in applications such as document classification, clustering, and information retrieval. Text mining typically involves converting text into a structured format, such as a vector space model, where documents are represented as vectors of word frequencies. This allows for efficient comparison and analysis of text data. Preprocessing steps include tokenization, filtering, lemmatization, and stemming to reduce the size of the dictionary and improve the quality of the text representation. Index term selection is also used to identify the most informative terms for classification or clustering tasks. The vector space model represents documents as vectors in a high-dimensional space, where each dimension corresponds to a word in the document collection. Similarity measures such as cosine similarity and Euclidean distance are used to compare documents. The model is enhanced by term weighting schemes that consider both the frequency of a word in a document and its rarity across the entire collection. Text mining methods include classification, clustering, and information extraction. Classification involves assigning pre-defined classes to documents, while clustering groups documents based on their similarity. Information extraction identifies specific information from text, such as named entities or relations. These methods are often evaluated using metrics such as accuracy, precision, recall, and F-score. The article also discusses related research areas such as information retrieval, natural language processing, and statistical learning, which contribute to the development of text mining techniques. The use of machine learning algorithms, such as Naïve Bayes, k-nearest neighbors, decision trees, and support vector machines, is highlighted for their effectiveness in text classification and clustering tasks. The article concludes with a discussion of the applications and challenges of text mining in various domains.This article provides an overview of text mining, a field that combines information retrieval, machine learning, statistics, computational linguistics, and data mining to extract useful information from unstructured text. Text mining involves preprocessing, classification, clustering, information extraction, and visualization to identify patterns and knowledge from text. It is closely related to knowledge discovery in databases (KDD), which focuses on finding valid, novel, and useful patterns in data. Text mining is often used in applications such as document classification, clustering, and information retrieval. Text mining typically involves converting text into a structured format, such as a vector space model, where documents are represented as vectors of word frequencies. This allows for efficient comparison and analysis of text data. Preprocessing steps include tokenization, filtering, lemmatization, and stemming to reduce the size of the dictionary and improve the quality of the text representation. Index term selection is also used to identify the most informative terms for classification or clustering tasks. The vector space model represents documents as vectors in a high-dimensional space, where each dimension corresponds to a word in the document collection. Similarity measures such as cosine similarity and Euclidean distance are used to compare documents. The model is enhanced by term weighting schemes that consider both the frequency of a word in a document and its rarity across the entire collection. Text mining methods include classification, clustering, and information extraction. Classification involves assigning pre-defined classes to documents, while clustering groups documents based on their similarity. Information extraction identifies specific information from text, such as named entities or relations. These methods are often evaluated using metrics such as accuracy, precision, recall, and F-score. The article also discusses related research areas such as information retrieval, natural language processing, and statistical learning, which contribute to the development of text mining techniques. The use of machine learning algorithms, such as Naïve Bayes, k-nearest neighbors, decision trees, and support vector machines, is highlighted for their effectiveness in text classification and clustering tasks. The article concludes with a discussion of the applications and challenges of text mining in various domains.