Abstract. The article discusses methods of intelligent text processing (Text Mining), which allow to transform poorly structured text data into structured and easily analysed information. With the growth of data volumes in the digital age, Text Mining is becoming an indispensable tool for analysing texts in various fields. These technologies find wide application in information security where text analysis helps to identify threats and anomalies, in healthcare to process medical records and extract diagnostic information, in marketing to analyse consumer preferences, and in legal practice where automation of document analysis improves accuracy and reduces time costs. The paper details both traditional statistical methods such as TF-IDF, Word2Vec, Latent Dirichlet Allocation (LDA) and state-of-the-art approaches including deep learning models based on transformer architecture such as BERT, GPT and their derivatives. The state-of-the-art methods show significant advances in context-awareness, semantics analysis, and extraction of hidden meanings from texts, which makes them indispensable for solving complex problems. Particular attention is paid to comparing the effectiveness of different methods and their applicability in automation tasks. The possibilities of Text Mining integration for analysing large amounts of data, identifying patterns and automating knowledge extraction processes are described. The presented research results emphasise the relevance of using these technologies to improve the efficiency of specialists’ work, accelerate the processes of information analysis and problem solving in key industries, which opens new perspectives for the implementation of intelligent data processing systems.

Keywords: Text Mining, intelligent text processing, machine learning, natural language processing, TF-IDF, Word2Vec, BERT, GPT, text analysis automation.