Research on Chinese Sentiment Analysis Based on Bi-LSTM Networks
Taozheng Zhang, Jiaqi Guo · 2021
Chinese sentiment analysis is a very important branch of natural language processing. It has been receiving much attention in recent years. The bidirectional long and short-term memory network (Bi-LSTM) model has been well applied in the field of sentiment analysis because of its own characteristics. This experiment hopes to further explore the performance and application of the Bi-LSTM model in sentiment analysis. There are three main steps in the experiment. First, the collected Chinese reviews are segmented and vectorized. Then, the Bi-LSTM is trained and tested. Finally, the sentiment analysis result is obtained. With the help of the hyper-parameter adjustment and the dropout mechanism, the evaluation indicators of the experimental model have reached about 89%. What's more, based on the same experimental environment and experimental data, this experiment tested the accuracy of CNN, LSTM, CNN_LSTM, and Bi-LSTM. In addition, the trained Bi-LSTM was used to analyze reviews from Taobao and JD.COM. The specific operation is to collect reviews on a certain product from Taobao and JD.COM to perform a specific analysis with the model. Then find the advantages and disadvantages of the model in practical applications, so that the model can continue to be improved.