Methods of Literary Text Analysis with the Help of Artificial Intelligence

Abstract

This paper examines modern methods of computer analysis of literary texts using artificial intelligence and natural language processing technologies. The study demonstrates how language models BERT, GPT, specialized libraries spaCy, Gensim and computing platforms can be adapted to solve traditional literary problems - from stylistic and thematic analysis to the study of narrative structures and intertextual connections. Particular attention is paid to the possibilities and limitations of using machine learning in the humanities, including problems of interpretability of results and the need to combine quantitative methods with qualitative philological analysis. The work systematizes existing digital tools for literary studies Voyant Tools, Mallet, Stylo, offering practical recommendations for their use in research practice. Particular emphasis is placed on the methodological aspects of integrating computer technologies into humanities research, where formal methods serve not as a replacement, but as a powerful addition to traditional approaches. The results of the study show that modern NLP technologies open up new prospects for analyzing large text corpora, identifying hidden patterns and posing new research questions in literary studies, while simultaneously requiring critical understanding and meaningful interpretation of the data obtained. The work is of interest to researchers at the intersection of digital humanities and computational linguistics, offering a comprehensive overview of current methods and their practical application in the analysis of literary texts.