Features of Recommendation Algorithm on Base of Analysis of Social Network Data Mining Methods

Abstract

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. SA allows the system to tailor recommendations based on sentiment analysis, offering users items that match their expressed emotions. Experiments show significant improvements in recommendation accuracy when sentiment data is integrated. TM allows the system to understand the main concerns of users by identifying dominant themes, thereby providing personalized recommendations and staying abreast of evolving trends. At the same time, AFM identifies influential users and communities, increasing relevance and awareness of system elements. The article highlights the enormous potential of social networks for the development of effective, personalized recommendation systems. Using sentiment analysis and topic modeling, these systems can provide personalized and relevant recommendations based on public sentiment, trending topics, and social network dynamics.