Exploiting topic based twitter sentiment for stock prediction

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

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 proceeding › Conference article published in proceeding or book › Academic research › peer-review

201 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 language	English
Title of host publication	Short Papers
Publisher	Association for Computational Linguistics (ACL)
Pages	24-29
Number of pages	6
ISBN (Print)	9781937284510
Publication status	Published - 1 Jan 2013
Externally published	Yes
Event	51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria Duration: 4 Aug 2013 → 9 Aug 2013

Publication series

Name	ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume	2

Conference

Conference	51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
Country/Territory	Bulgaria
City	Sofia
Period	4/08/13 → 9/08/13

ASJC Scopus subject areas

Language and Linguistics
Linguistics and Language

Cite this

@inproceedings{451e4ad597ca4d54b6251eeb5ca8e050,

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.",

author = "Jianfeng Si and Arjun Mukherjee and Bing Liu and Qing Li and Huayi Li and Xiaotie Deng",

year = "2013",

month = jan,

day = "1",

language = "English",

isbn = "9781937284510",

series = "ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",

publisher = "Association for Computational Linguistics (ACL)",

pages = "24--29",

booktitle = "Short Papers",

note = "51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 ; Conference date: 04-08-2013 Through 09-08-2013",

}

Si, J, Mukherjee, A, Liu, B, Li, Q, Li, H & Deng, X 2013, Exploiting topic based twitter sentiment for stock prediction. in Short Papers. ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, vol. 2, Association for Computational Linguistics (ACL), pp. 24-29, 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, Sofia, Bulgaria, 4/08/13.

Exploiting topic based twitter sentiment for stock prediction. / Si, Jianfeng; Mukherjee, Arjun; Liu, Bing et al.
Short Papers. Association for Computational Linguistics (ACL), 2013. p. 24-29 (ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference; Vol. 2).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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 - 2013/1/1

Y1 - 2013/1/1

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.

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.

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M3 - Conference article published in proceeding or book

AN - SCOPUS:84906932179

SN - 9781937284510

T3 - ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

SP - 24

EP - 29

BT - Short Papers

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T2 - 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

Y2 - 4 August 2013 through 9 August 2013

ER -

Exploiting topic based twitter sentiment for stock prediction

Abstract

Publication series

Conference

ASJC Scopus subject areas

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