Abstract
Behavioral economics and behavioral finance believe that public mood is correlated with economic indicators and financial decisions are significantly driven by emotions. A growing body of research has examined the correlation between stock market and social media public mood state. However most research is conducted on English social media websites, the number of research on how public mood states in Chinese social media websites affect the stock market in China is limited. This paper first summarizes the previous research on text mining and social media sentiment analysis. After that, we investigate whether measurements of collective public mood states derived from Weibo which is a social media website similar as Twitter but most posts are written in Chinese are correlated to the stock market price in China. We use a novel Chinese mood extracting method using two NLP (Natural Language Processing) tools: Jieba and Chinese Emotion Words Ontology to analyze the text content of daily Weibo posts. A Granger Causality analysis is then used to investigate the hypothesis that the extracted public mood or emotion states are predictive of the stock price movement in China. Our experimental results indicate that some public mood dimensions such as “Happiness” and “Disgust” are highly correlated with the change of stock price and we can use them to forecast the price movement.
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Chen, W.H., Cai, Y., Lai, K.K. (2016). Weibo Mood Towards Stock Market. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_1
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DOI: https://doi.org/10.1007/978-3-319-32055-7_1
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