Sentiment analysis of Chinese comments on OTA website using BERT and LSTM
Juan Shen, Mei Xu · International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022) · 2022
Under the background of "Internet + tourism", many OTA sites have large numbers of users and have accumulated huge amounts of user review information. In this paper, web crawler is used to grab Chinese comment data on OTA website for pre-processing. BERT pre-training model is used to represent text feature vectors at sentence level. The results are then input into LSTM model to obtain high-level semantic features. Thus, the emotional tendency of the comments can be classified. The comparison of experimental data shows that BERT-LSTM model has higher classification accuracy, recall rate and F1 value. It shows that the effectiveness of text sentiment analysis has been improved. An emotional analysis of these emotional commentary texts is conducive to the monitoring of management departments, providing reference for the government and enterprises to make decisions. And this research has very high application value for promoting the development of tourism.