Exploiting topic based twitter sentiment for stock prediction

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

135 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationShort Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages24-29
Number of pages6
ISBN (Print)9781937284510
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
Duration: 4 Aug 20139 Aug 2013

Publication series

NameACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume2

Conference

Conference51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
CountryBulgaria
CitySofia
Period4/08/139/08/13

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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