A study of text classification algorithms for live-streaming e-commerce comments based on improved BERT model - Rong Zhou; Qing Shen; HuaFeng Kong (PLoS ONE, 2025)
As e-commerce live streaming becomes increasingly popular, the textual analysis of bullet comments is becoming more and more important. Bullet comments is characterized by its brevity, diverse content, and vast quantity. Faced with these challenges, this study proposes an improved BERT model based on a hierarchical structure for classifying e-commerce bullet comments. First, a parent class BERT model is trained to categorize bullet comments into six designated categories (parent categories). Subsequently, subclass BERT models are trained to classify bullet comments into subcategories. The model combines BERT’s profound semantic comprehension with the closely categorized capabilities of the hierarchical structure. Empirical evidence shows that the proposed model significantly improves classification accuracy and efficiency, aiding in further analysis of bullet comments, extracting valuable information, and achieving effective marketing.