Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model - Ming-Fang Li; Guo-Xiang Zhang; Lu-Tao Zhao; Tao Song (Journal of King Saud University: Computer and Information Sciences)
A BERT-MDLP-Bayesian Network model (BMB) is proposed to analyze the improvement strategy of e-commerce products based on user generated content (UGC). The proposed model can be represented into four parts: clearing redundant data on the obtained UGC, extracting product attributes and word vector to generate product attributes, establishing product attribute Bayesian network corresponding to UGC, and inferring the causal relationship between product attributes. In order to verify the effectiveness of the proposed model, an amazon tablet product is used for empirical analysis. Compared with the traditional model, BMB model has better performance in product feature mining in three aspects of feature diversity, feature long tail and attribute difference. In application, the model can effectively describe the core problems of products, and provide suggestions for e-commerce to modify marketing strategies and determine the new direction of product development.