Emotion correction for weibo users via weakly supervised learning and semantic understanding.

With the fast pace of modern society, individuals are facing increasing psychological stress and emotional challenges. As an important platform for public emotional expression, Weibo contains extensive textual data reflecting users' emotional states. This paper proposes an emotion correction approach for Weibo users that integrates weakly supervised learning with semantic understanding. The approach combines emotion recognition, emotion-cause classification, and personalized content recommendation, where the emotional polarity of Weibo texts is first identified, negative posts are then classified into corresponding emotion-cause categories, and positively oriented content related to users' emotion causes is subsequently recommended to provide targeted emotional support. Experiments on a real-world Weibo dataset show that the proposed approach achieves accuracies of 96.2% and 83.5% in emotion recognition and emotion-cause classification, respectively, while also delivering strong recommendation performance. These results demonstrate the potential of the proposed model for supporting users' emotional correction and contributing to mental health.