In this paper, we design an integrated algorithm to evaluate the sentiment of
Chinese market. Firstly, with the help of the web browser automation, we crawl
a lot of news and comments from several influential financial websites
automatically. Secondly, we use techniques of Natural Language Processing(NLP)
under Chinese context, including tokenization, Word2vec word embedding and
semantic database WordNet, to compute Senti-scores of these news and comments,
and then construct the sentimental factor. Here, we build a finance-specific
sentimental lexicon so that the sentimental factor can reflect the sentiment of
financial 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 correlation
between our standard sentimental factor and the Chinese market, and the
adjusted factor is even more informative, having a stronger correlation with
the Chinese market. Therefore, our sentimental factors can be important
references when making investment decisions. Especially during the Chinese
market crash in 2015, the Pearson correlation coefficient of adjusted
sentimental factor with SSE is 0.5844, which suggests that our model can
provide a solid guidance, especially in the special period when the market is
influenced greatly by public sentiment.

本文提出了一种整合算法，通过使用 Web 浏览器自动化技术抓取多个有影响力的金融网站的许多新闻和评论，在中文语境下，利用自然语言处理技术计算这些新闻和评论的情感分数，并构建了一个与金融特定情感词库相匹配的情感因子，可以在投资决策时提供重要的参考价值。