Exploring Consumption Intent in Live E-Commerce Barrage: A Text Feature-Based Approach Using BERT-BiLSTM Model

Abstract

E-commerce with the immersive scene of live streaming gives users a new interaction and consumption experience, extending a new path for traditional marketing. Live barrage, being the primary means of users’ social engagement in live e-commerce, encapsulates their inclination to purchase products, commonly referred to as consumption intention. The mining of live barrage enables the exploration of latent user behavior and consumption intention. The current methods employed for mining consumption intention in e-commerce live barrage encounter challenges including insufficient corpus, excessively brief text, and significant semantic deficiencies. Therefore, this study proposes a paradigm of consumption intention recognition by combining the text features of barrage: by constructing a dataset of Live barrage, cleaning the original dataset, and then using a fused BERT’s feature network model BERT-BiLSTM to identify the consumption intention in live e-commerce Live barrage. The experimental results demonstrate the efficacy of the proposed method for recognizing consumption intention. Finally, this study conducted text mining on live barrage containing consumption intent, including word cloud visualization, topic extraction, and co-occurrence analysis, further investigating consumers’ purchasing needs and intentions.

Keywords

• BERT-BiLSTM
• intention identification
• live streaming barrage
• text mining