E-Commerce Network Search System Based on Target Webpage Positioning and Sentiment Analysis Recommendation

Abstract

With the rise of e-commerce network search systems, product search efficiency and user satisfaction have become increasingly important. To address the low accuracy of consumer sentiment analysis in existing product recommendation scenarios, a webpage localization and sentiment analysis recommendation model is proposed that combines an improved web search algorithm with a bidirectional long short-term memory network and an attention mechanism. An e-commerce network search system is then designed around this model. Experimental results show that the sentiment analysis recommendation model achieves an accuracy of 98.88% and an average mean squared error of 1.027, outperforming all comparison models. The average root-mean-square error is 0.476, recall is 98.92%, the F1 score is 97.78%, and the recognition accuracy for each of the four emotional tendencies exceeds 95%. In addition, the integrated system delivers an average search time of 87.6 ms, a central processing unit occupancy of 44.68%, a missed-search rate of 1.42%, and a user satisfaction of 99.34%, all superior to the comparison systems. The system offers a ready-to-deploy solution for sentiment-aware product search and provides a theoretical basis for future research in e-commerce search systems.