Discovering public sentiment in social media for predicting stock movement of publicly listed companies
作者：
Highlights：
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• Combined with both direct and indirect division operations, our proposed algorithm has achieved better predict accuracy compared with other existed direct social media mining method through the experiment results.
• The proposed algorithms have a better prediction performance in some certain industries such as IT and media.
• Our study indicates the proposed algorithms have a better performance in using current tweets’ sentiment to predict the stock price of three days later.
摘要
•Combined with both direct and indirect division operations, our proposed algorithm has achieved better predict accuracy compared with other existed direct social media mining method through the experiment results.•The proposed algorithms have a better prediction performance in some certain industries such as IT and media.•Our study indicates the proposed algorithms have a better performance in using current tweets’ sentiment to predict the stock price of three days later.
论文关键词：Social media analysis,Twitter,Stock prediction,Data mining,Sentiment analysis,Big data,SMeDA-SA,Parallel architecture
论文评审过程：Received 6 September 2014, Revised 26 June 2016, Accepted 13 October 2016, Available online 2 February 2017, Version of Record 8 May 2017.
论文官网地址：https://doi.org/10.1016/j.is.2016.10.001
