Research on Sentiment Analysis of E-commerce User Evaluation Content Based on Deep Learning

Abstract

This paper proposes an e-commerce user review sentiment analysis model based on BERT-BiLSTM-Attention structure, which incorporates BiLSTM to extract bi-directional temporal features on the basis of BERT contextual semantic modeling and introduces an attention mechanism to enhance the ability of focusing on key information. The model is trained and tested on a 300,000 reviews dataset, comparing the models of BERT, TextCNN, and BiGRU. The accuracy of the fusion model on the test set reaches 0.912, and the F1 value is 0.913, which is 1.9% higher than the BERT model, and 6.2% higher than TextCNN, verifying its superior effectiveness and stability in recognizing sentiment polarity. The results demonstrate the model's strong potential for practical application.