Exploiting Topic based Twitter Sentiment for Stock Prediction

Jianfeng Si, Arjun Mukherjee, Bing Liu, Qing Li, Huayi Li, Xiaotie Deng

Abstract: 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.