Exploiting Topic based Twitter Sentiment for Stock Prediction
Jianfeng Si, Arjun Mukherjee, Bing Liu, Qing Li, Huayi Li and xiaotie Deng
The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013
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 opinion 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.
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