Applying machine learning to social media analysis: opportunities and challenges

Abstract

This article applies a machine learning approach to analyzing social media, which has a decisive impact on user behavior, public sentiment, and current trends. The key pillars of the approach include natural language analysis, text classification, sentiment analysis, and the identification of hidden patterns. Particular attention is given to the practical applications of such technologies in marketing, political analysis, public opinion monitoring, and reputation management. Along with the benefits, key challenges are discussed, such as data quality issues, privacy concerns, ethical limitations, and model interpretability. A conclusion is reached regarding the need for a comprehensive approach to implementing machine learning in social media analysis, taking into account technological and social factors.