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  3. Volume 18 (Part 2), 2025
  4. Information Technology for Text Classification Tasks Using Large Language Models

Information Technology for Text Classification Tasks Using Large Language Models

SISN.
2025;
Volume 18
: pp. 201 - 214
Authors:
  1. Maksym Yaromych
1
Lviv Polytechnic National University Department of Applied Linguistics, Ukraine

The article addresses the problem of text classification in the context of growing information flows and the need for automated content analysis. A universal information technology is proposed, combining classical machine learning methods with the potential of Large Language Models for processing news, scientific, literary, journalistic and legal texts. Using the BBC News corpus (2225 texts), k-means clustering with TF-IDF demonstrated clear thematic grouping. The scientific contribution lies in the development of a methodological framework capable of transitioning from statistical to semantic classification models. The technology can be implemented in education, research, media and legal anal- ytics. Future directions include multimodal data integration and explainability mechanisms for decision- support systems.

text classification
information technology
k-means
TF-IDF
Large Language Models
semantic analysis
automated text monitoring
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