TY - GEN
T1 - Exploiting topic based twitter sentiment for stock prediction
AU - Si, Jianfeng
AU - Mukherjee, Arjun
AU - Liu, Bing
AU - Li, Qing
AU - Li, Huayi
AU - Deng, Xiaotie
PY - 2013/8
Y1 - 2013/8
N2 - 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. © 2013 Association for Computational Linguistics.
AB - 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. © 2013 Association for Computational Linguistics.
UR - http://www.scopus.com/inward/record.url?scp=84906932179&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84906932179&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781937284510
VL - 2
SP - 24
EP - 29
BT - ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PB - Association for Computational Linguistics
T2 - 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
Y2 - 4 August 2013 through 9 August 2013
ER -