AI-POWERED TEXT ANALYSIS SYSTEMS

The article explores the development, principles, and applications of AI-powered text analysis systems as a response to the exponential growth of unstructured information in the modern digital environment.It outlines the historical trajectory from manual information retrieval towards highly automated intelligent systems, emphasizing how artificial intelligence has transformed text processing into a structural, semantic, and functional discipline within linguistics and computational science.The study employs a comprehensive methodological framework, integrating analysis and synthesis, taxonomy, induction and deduction, comparative and contextual methods, corpus approaches, distributive and component analysis, as well as frame and compositional analysis.Drawing on English and Ukrainian media texts, the research highlights the acute challenges of information overload, where unstructured data accounts for nearly 90% of available online content, making effective retrieval and selection of relevant information increasingly difficult.The discussion reviews key AI-driven tools such as OBSERVER, OntoSeek, and TextAnalyst, demonstrating their ability to build semantic networks, thematic hierarchies, conceptual graphs, and perform automated clustering, indexing, and semantic search.Particular attention is given to the integration of metadata-based technologies like the Open Archives Initiative (OAI-PMH), which enable unified access to distributed text repositories.The article systematically describes the multi-level process of AI text analysis, including graphematic segmentation, morphological parsing, syntactic structuring, and semantic modeling.While these technologies represent a significant advance in enabling intelligent information retrieval, they continue to face major limitations in accuracy, semantic understanding, and contextual relevance due to the complexity of natural language.The findings indicate that current AI-powered systems remain constrained in their ability to achieve deep syntacticsemantic comprehension, often producing results with insufficient correlation to user queries.This underscores the necessity for developing new approaches that better capture structural and semantic features of natural language, as well as improved databases capable of handling vast unstructured text arrays.The article concludes that future progress in AI-powered text analysis will depend on advancing semantic algorithms, refining linguistic modeling, and addressing the critical problem of structuring the digital information space.