семантичний аналіз

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.

In recent years, social media platforms have become powerful data collection tools to improve user experience. The vast amount of data generated through social media provides a unique opportunity to develop innovative recommendation systems. This article analyzes the application of data mining methods for social networks in the context of effective recommendation systems, focusing on three key methodologies: sentiment analysis (SA), topic modeling (TM) and social network analysis (SNA), highlighting their positive features.