Paper: Exploiting Topic based Twitter Sentiment for Stock Prediction

ACL ID P13-2005
Title Exploiting Topic based Twitter Sentiment for Stock Prediction
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market. We first utilize a con- tinuous Dirichlet Process Mixture model to learn the daily topic set. Then, for each topic we derive its sentiment according to its opin- ion words distribution to build a sentiment time series. We then regress the stock index and the Twitter sentiment time series to predict the market. Experiments on real-life S&P100 Index show that our approach is effective and performs better than existing state-of-the-art non-topic based methods.