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In this paper, we design an integrated algorithm to evaluate the sentiment ofChinese market. Firstly, with the help of the web browser automation, we crawla lot of news and comments from several influential financial websitesautomatically. Secondly, we use techniques of Natural Language Processing(NLP)under Chinese context, including tokenization, Word2vec word embedding andsemantic database WordNet, to compute Senti-scores of these news and comments,and then construct the sentimental factor. Here, we build a finance-specificsentimental lexicon so that the sentimental factor can reflect the sentiment offinancial market but not the general sentiments as happiness, sadness, etc.Thirdly, we also implement an adjustment of the standard sentimental factor.Our experimental performance shows that there is a significant correlationbetween our standard sentimental factor and the Chinese market, and theadjusted factor is even more informative, having a stronger correlation withthe Chinese market. Therefore, our sentimental factors can be importantreferences when making investment decisions. Especially during the Chinesemarket crash in 2015, the Pearson correlation coefficient of adjustedsentimental factor with SSE is 0.5844, which suggests that our model canprovide a solid guidance, especially in the special period when the market isinfluenced greatly by public sentiment.

Constructing Financial Sentimental Factors in Chinese Market Using Natural Language Processing