Explainable Dual-Branch Combination Network With Key Words Embedding and Position Attention for Sentimental Analytics of Social Media Short Comments

Abstract

Social media platforms such as Weibo and TikTok have become more influential than traditional media. Sentiment in social media comments reflects users’ attitudes and impacts society, making sentiment analysis (SA) crucial. AI driven models, especially deep-learning models, have achieved excellent results in SA tasks. However, most existing models are not interpretable enough. First, deep learning models have numerous parameters, and their transparency is insufficient. People cannot easily understand how the models extract features from input data and make sentiment judgments. Second, most models lack intuitive explanations. They cannot clearly indicate which words or phrases are key for emotion prediction. Moreover, extracting sentiment factors from comments is challenging because a comment often contains multiple sentiment characteristics. To address these issues, we propose a dual-branch combination network (DCN) for SA of social media short comments, achieving both word-level and sentence-level interpretability. The network includes a key word feature extraction network (KWFEN) and a key word order feature extraction network (KWOFEN). KWFEN uses popular emotional words and SHAP for word-level interpretability. KWOFEN employs position embedding and an attention layer to visualize attention weights for sentence-level interpretability. We validated our method on the public dataset weibo2018 and TSATC. The results show that our method effectively extracts positive and negative sentiment factors, establishing a clear mapping between model inputs and outputs, demonstrating good interpretability performance.