Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of documents on public and social network websites. Therefore, this chapter integrates political blogs and news articles to develop a public mood dynamic prediction model for the stock market, while referencing the behavioral finance perspective and online political community characteristics. The goal of this chapter is to apply a big data and opinion mining approach to a sentiment analysis for the relationship between political status and economic development in Taiwan. The proposed model is verified using experimental datasets collected from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news, covering the period from January 1, 2016 to June 30, 2017. The empirical results indicate the accuracy rate with which the proposed model forecasts stock prices.
Chen, M.-Y., Fan, M.-H., Chen, T.-H. and Hsieh, R.-P. (2019), "Modeling Public Mood and Emotion: Blog and News Sentiment and Politico-economic Phenomena", Visvizi, A. and Lytras, M.D. (Ed.) Politics and Technology in the Post-Truth Era (Emerald Studies in Politics and Technology), Emerald Publishing Limited, Bingley, pp. 57-71. https://doi.org/10.1108/978-1-78756-983-620191005
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