Sentiment Analysis of Board Secretaries’ Q&R Data

Abstract

In the Internet era, due to the rapid development of investors communication with public companies, people have diversified ways to express their opinions, thus generating a large amount of data, which contains valuable information. In this paper, we use a combination of the financial sentiment dictionary and Bert to analyze the sentiment of investors’ questions based on the Q&R data of board secretaries on the platform "Easy Interactive" (http://irm.cninfo.com.cn/) launched by Shenzhen Stock Exchange, and the final accuracy rate is 92%, which is 16% higher than the traditional sentiment analysis methods. Compared with offline research, financial news, stock forums, social software, and other data, the Q&R data selected in this paper has less noise and is more intuitive. Moreover, this paper considers knowledge in the financial domain in sentiment analysis and has domain friendliness and model generalization in the financial domain by combining the financial domain sentiment lexicon with the Bert model with adversarial training.