The risk of peer to peer lending (P2P) platform is predicted based on text data on the Internet to avoid the risk of social network financing and improve the security of social network financing. First, the transaction and review text information of a third-party P2P platform are classified for the time series of emotional changes. Second, the Granger causal relation test is used to verify the correlation between the time series of emotional changes and trading volume. Finally, a long short-term memory (LSTM) forecasting model is proposed based on investors’ emotional changes to predict the trading volume of P2P platforms using emotional changes as a reference for social network financing to avoid risks. The results show that the value of Pearson correlation coefficient between the trading volume of P2P platforms and negative emotions is -0.2088, with a P value less than 1%, indicating a correlation between emotional changes and trading volume. The Pearson correlation coefficient between the predicted and actual values is 0.7995, whereas the mean square error is 0.2190 with a fitting degree of 0.6532. This shows that the LSTM forecasting model can accurately predict the trading volume of P2P platforms with good performance in comparison with other forecasting models.

基于互联网上的文本数据预测P2P平台的风险，规避社交网络融资的风险，提高社交网络融资的安全性。首先，针对情绪变化的时间序列对第三方P2P平台的交易和评论文本信息进行分类。其次，利用格兰杰因果关系检验来验证情绪变化时间序列与交易量之间的相关性。最后，提出基于投资者情绪变化的长短期记忆（LSTM）预测模型，以情绪变化作为社交网络融资规避风险的参考来预测P2P平台的交易量。结果显示，P2P平台交易量与负面情绪之间的Pearson相关系数值为-0.2088，P值小于1%，表明情绪变化与交易量之间存在相关性。预测值与实际值之间的 Pearson 相关系数为 0.7995，均方误差为 0.2190，拟合度为 0.6532。这表明LSTM预测模型能够准确预测P2P平台的交易量，与其他预测模型相比，具有良好的性能。