Modeling public mood and emotion: Blog and news sentiment and socio-economic phenomena
Future Generation Computer SystemsPublished 3 November 2017
Mu‐Yen Chen, Ting-Hsuan Chen
Citations53
SJR quartileQ1
SJR score1.55
SNIP2.23
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TL;DR
Empirical results indicate the big data analysis techniques to assess emotional content of commentary on current stock or financial issues can effectively forecast stock price movement.
Abstract
The development of online virtual communities has raised the importance in analyzing massive volume of text from websites and social networks. This research analyzed financial blogs and online news articles to develop a public mood dynamic prediction model for stock markets, referencing the perspectives of behavioral finance and the characteristics of online financial communities. This research applies big data and opinion mining approaches to the investors' sentiment analysis in Taiwan. The proposed model was verified using experimental datasets from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news over an 18 month period. Empirical results indicate the big data analysis techniques to assess emotional content of commentary on current stock or financial issues can effectively forecast stock price movement.
Keywords
Computer ScienceDecision Sciences
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