E-Commerce Live Streaming Danmaku Classification Through LDA-Enhanced BERT-TextCNN Model

Abstract

With the increasing popularity of e-commerce live streaming, comprehensive analysis of live barrage text has become increasingly crucial. This study presents a thematic analysis method for categorizing e-commerce live streaming barrage text using latent Dirichlet allocation (LDA) topic modeling, combined with the advantages of the Bidirectional Encoder Representations from Transformers (BERT) and TextCNN models. The LDA algorithm is initially used to extract topics from the barrage text, and then a dataset comprising six designated categories is assembled. Subsequently, a BERT-TextCNN hybrid model is trained, merging BERT's profound semantic comprehension with TextCNN's ability to extract local features. Empirical evidence shows that the proposed model notably improves classification accuracy and efficiency, providing valuable theoretical and practical insights for crafting strategies and optimizing user experience on e-commerce live streaming platforms.