Sentiment Analysis of Social Support on Treads of Social Media Using Deep Learning Approach

This research aims to automatically classify social support threads shared by users on public social network platforms. Recognizing the growing role of social platforms in providing emotional support, this study focuses on identifying different support types using sentiment analysis. By incorporating data augmentation, three datasets were created for this analysis. We used existing models to compare classification performance. The results indicate that while data augmentation can sometimes improve model performance, the quality and context of the original data are crucial for accurately identifying emotional support. The study also found that current models struggle to distinguish subtle differences between support categories, especially when the data is imbalanced. These findings highlight the limitations of current AI models and the need for more balanced datasets and better classification techniques. This research contributes to the development of automated social support classification and provides a foundation for future studies to improve model accuracy using data augmentation and advanced methods.