Supporting information data.

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.

随着电商直播的日益普及，弹幕（bullet comments）的文本分析愈发重要。弹幕具有篇幅简短、内容多元、数量庞大的典型特征。面对上述诸多挑战，本研究提出一种基于层级结构的改进型BERT模型，用于电商直播弹幕的分类任务。首先，训练父类BERT模型，将弹幕划分为六大预设类别（父类别）；随后，训练子类BERT模型，将弹幕归类至对应子类别中。该模型融合了BERT出色的语义理解能力与层级结构的精细化分类优势。实证结果表明，所提出的模型显著提升了分类准确率与效率，可为后续弹幕分析、提取有价值信息及开展有效营销提供有力支撑。