Research on investor sentiment and stock market prediction based on Weibo text Microblog can obtain investorsu0027 views of stock market accurately and timely, and grasping the fluctuation of investor sentiment is beneficial to predict the future trend of stock market. Based on behavioral finance theory, this paper uses text mining and natural language processing technology to obtain investor sentiment, and then combines price earnings ratio and turnover rate to build a stock market prediction model. The results show that the investor sentiment based on Weibo text has a certain predictive ability to the Shanghai stock index. The model has the best performance in the ascending period, and the effect of the shock period is the worst.

User and Topic Hybrid Context Embedding for FinanceRelated Text Data Mining As large amounts of financerelated microblogs, posted by both professional and amateur investors, are mingling in online social networks that could bring impact to real financial markets, itu0027s an essential task to analyze financial texts posted by different users. Besides that, as barely nobody could be an allround expert in various financial products such as stocks, futures and bonds, topic preferences of different users should also be considered in these mining tasks. That is to say, financial text contents should be analyzed in both user and topic level. In recent advances of deep learning technology, learning the embedding representation of contents is a stateofart method in financial text mining over handcrafted feature extraction techniques. Along this line, in this paper we propose a hybrid embedding method that both the embedding representation of users and the embedding representation of financial topics are learned from the posted financerelated contents in online social networks. Following the commonly used methods in embeddingrelated researches, we apply extensive experiments with realworld datasets to show the effectiveness of our hybrid user and topic embedding (UTE) approach, in both intrinsic and extrinsic ways. Itu0027s shown that our approach can intrinsically distinguish social network users with their learned embeddings, so they can be grouped into humanexplainable clusters. Moreover, we also propose a case study on sentiment analysis by applying our user and topic hybrid embedding with a deep contextual neural network architecture. The results prove that our approach outperforms other baseline methods in financialrelated sentiment analysis, and hopefully for other downstream text mining tasks as well.