Research on the Application of Social Media Data Mining Based on Sentiment Analysis

As social software becomes increasingly prevalent, social media platforms have evolved into a major avenue for individuals to express their emotions. On these platforms, users convey their emotions through text, images, and videos. Their expressions not only reveal their personal emotional state but also correlate with subsequent behavioral patterns. As an invaluable tool for understanding human psychology, it is able to identify emotional tendencies and make correlation predictions by analyzing vast quantities of user data on social media. Recently, sentiment analysis methods and applications have evolved significantly, spanning fields like marketing, public relations, and social research. However, it continues to encounter significant challenges in complex contexts and cross-cultural communication. To gain a deeper understanding of the applications of sentiment analysis in social media, the paper surveys the principal methods and domains of sentiment analysis in recent years by literature investigation, with particular attention to multidisciplinary case studies. The findings indicate that sentiment analysis techniques are effective in some areas like real-time monitoring and trend prediction, which are vital for business decision-making and planning. Meanwhile, sentiment analysis is increasingly being applied to areas such as mental health monitoring as well as social opinion analysis. Nevertheless, current methods still struggle with complex contexts and cross-cultural sentiment expressions, necessitating further research and improvement.