Organization of software and neural network algorithms for machine analysis of textual data presented in natural language

The article addresses the problem of organizing software and neural network algorithms for natural language processing. Given the rapid growth of information flows and the limitations of computational resources, optimizing methods for processing short, unstructured messages and complex structured documents has become especially urgent. This study aims to develop a comprehensive method for organizing computer-aided analysis of text data, ensuring a balance between the accuracy of results and the efficiency of computational resource use. The research systematically examines modern approaches to tokenization, clustering, semantic-relevant search, and deep learning architectures, with particular attention to their adaptability under resource-constrained conditions. A multilevel methodology is suggested, combining the preliminary classification of text arrays, semantic clustering, and the use of a Bidirectional LSTM neural network model. Practical implementation of the method was tested through an automated text analysis application, demonstrating stable reduction in the loss function and acceptable resource consumption. The ability to adapt to different types of text data, reduced resource consumption while maintaining high-quality analysis, and suitability for deployment in environments with low computing capacity should be considered the main advantages of the developed approach. The scientific novelty of the article is substantiated by the integration of semantic-relevant clustering and lightweight deep learning techniques into a single optimized framework. The practical value of the study is the possibility of applying the proposed methodology in real-world information systems where limited hardware capabilities require efficient and adaptive text processing solutions.