E-commerce recommender system design based on web information extraction and sentiment analysis.

ABSTRACT: The research proposes an e-commerce recommendation system based on web page information extraction and sentiment analysis. Through the improved S-PageRank algorithm and the dynamic topic library generation strategy, the precision rate of cross-platform commodity information extraction has been significantly improved to 90%, which is superior to the traditional S-PageRank algorithm. The template-based web page information extraction method performs well, with a precision rate 10% higher than that of the method based on the document object model. In terms of sentiment analysis, the comprehensive attention mechanism model combining the topic model and the bidirectional long short-term memory network has achieved the precise calculation of the sentiment scores of each topic in the customer eva

SUBMITTER: Feng J 

PROVIDER: S-EPMC12456782 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature