BERT-BiLSTM-Attention model for sentiment analysis on Chinese stock reviews

Abstract COVID-19 has produced significant fluctuations and impacts on the Chinese stock market, and the sentiment analysis of stock reviews is important for the study of economic recovery. Due to the lack of a large amount of labeled data in the existing Chinese stock review data, and the currently popular Bert model mostly failed to consider contextual information according to different contextual backgrounds when extracting features, resulting in the lack of contextual information in the modeled features. To address the above problems, this paper proposes an innovative Chinese stock review sentiment analysis model BERT-BiLSTM-Attention, which encodes the stock review text by BERT to enhance the semantic feature representation of the text, BiLSTM is then utilized to enhance the contextual information of the overall context of the review as well as the model’s comprehension of the text sequences, and then Attention mechanism is utilized to obtain important textual information and get the most effective information quickly. Experiments show that the model is effective in sentiment analysis of Chinese stock reviews, with an accuracy of 93.98%, It can be proved that the proposed model well enhances the performance of stock review text classification, and has a strong generalization ability, which can be used for sentiment analysis in many fields.