Predicting Sector Index Movement with Microblogging Public Mood Time Series on Social Issues
Yujie Lu, Jinlong Guo, Kotaro Sakamoto, Hideyuki Shibuki, Tatsunori Mori · Institutional Repositories DataBase (IRDB) · 2015
This paper develops a technique that unfolds public mood on social issues from real-time so-cial media for sector index prediction. We first propose a low-dimensional support vector ma-chine (SVM) classifier using surrounding infor-mation for twitter sentiment classification. Then, we generate public mood time series by aggre-gating message-level weighted daily mood (WDM) based on the sentiment classification re-sults. Lastly, we evaluate our method against the real stock index in two kinds of time periods (fluctuating and monotonous) separately using static cross-correlation coefficient (CCF) and dynamic vector auto-regression (VAR). The ex-periments on “food safety ” issue show that the proposed WDM method outperforms the word-level baseline method in predicting stock move-ment, especially during fluctuating period. 1