Abstract: There are many kinds of studies on the trading volume of Peer-to-Peer market.However,the common me-thods only take investor and market information as characteristics,and donot consider the relationship between investor sentiment changes and the market.The research shows that investors’ sentiments have a profound impact on their investment decisions and behaviors.Therefore,according to the financial theory,this paper proposed a method to predict the trading volume of Peer-to-Peer market based on investor’s sentimental tendency.Firstly,the comments of WangDaiZhiJia is taken as the research object and applied TextCNN model for sentiment classification.The time series of sentiment tendency is obtained,so as to achieve the purpose of measuring the trend of investor sentiment.Secondly,it verifies the relationship between investor’s emotion time series and trading volume index through Granger causality test and Pearson correlation coefficient.Finally,a predictive model based on long short term memory network is employed to predict the trading volume of the Peer-to-Peer market.The experimental results show that by adding sentimental features to the trading volume prediction model,the predictive ability of the model is improved significantly.

Key words: Deep learning, Natural language processing, Peer-to-Peer lending, Sentiment classification