Sentiment Analysis for E-commerce Product Reviews by Deep Learning Model of Bert- BiGRU-Softmax

The sentiment analysis has the key problem for the e-commerce product quality management, which customers know their people’s attitudes on interested products by e-commerce reviews. Meanwhile, manufacturers are able to learn the public sentiment on their products being sold in E-commerce platforms. This paper proposed the deep learning models of Bert-BiGRU-Softmax, which uses the input layer of Sentiment Bert model to extract multi-dimensional of E-commerce reviews, the hidden layer of Bidirectional GRU model to obtain semantic codes and calculate representing weights of reviews, the Softmax with attention mechanism as the output layer to classify the sentiment tendency. We conduct experiments on a large-scale dataset involving over 500 thousand reviews compared with different learning models. The experimental results show that the proposed models reach the high accuracy 95.5% on the E-commerce reviews, and the outperforms of RNN, BiGRU, Bert-BiLSTM in terms of accuracy and loss.