@inproceedings{f42e89f896624fe0bba1f33abe12a0b0,
title = "Exploiting topic based twitter sentiment for stock prediction",
abstract = "This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market. We first utilize a continuous 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. {\textcopyright} 2013 Association for Computational Linguistics.",
author = "Jianfeng Si and Arjun Mukherjee and Bing Liu and Qing Li and Huayi Li and Xiaotie Deng",
year = "2013",
month = aug,
language = "English",
isbn = "9781937284510",
volume = "2",
publisher = "Association for Computational Linguistics",
pages = "24--29",
booktitle = "ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
address = "United States",
note = "51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 ; Conference date: 04-08-2013 Through 09-08-2013",
}