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。

利用自然语言处理构建中国市场金融情感因子

Constructing Financial Sentimental Factors in Chinese Market Using  Natural Language Processing

Bitcoin has emerged as a fascinating phenomenon of the financial markets.
Without any central authority issuing the currency, it has been associated with
controversy ever since its popularity and public interest reached high levels.
Here, we contribute to the discussion by examining potential drivers of Bitcoin
prices ranging from fundamental to speculative and technical sources as well as
a potential influence of the Chinese market. The evolution of the relationships
is examined in both time and frequency domains utilizing the continuous
wavelets framework so that we comment on development of the interconnections in
time but we can also distinguish between short-term and long-term connections.

本研究分析了比特币价格的可能驱动因素，包括基本面、投机性和技术性来源，以及中国市场的影响。同时，我们还应用了连续小波分析框架考察了关系的时域和频域演化。