Exploiting social relations and sentiment for stock prediction

Jianfeng Si, Arjun Mukherjee, Bing Liu, Sinno Jialin Pan, Qing Li, Huayi Li

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

43 Citations (Scopus)

Abstract

In this paper we first exploit cash-tags ("$" followed by stocks' ticker symbols) in Twitter to build a stock network, where nodes are stocks connected by edges when two stocks co-occur frequently in tweets. We then employ a labeled topic model to jointly model both the tweets and the network structure to assign each node and each edge a topic respectively. This Semantic Stock Network (SSN) summarizes discussion topics about stocks and stock relations. We further show that social sentiment about stock (node) topics and stock relationship (edge) topics are predictive of each stock's market. For prediction, we propose to regress the topic-sentiment time-series and the stock's price time series. Experimental results demonstrate that topic sentiments from close neighbors are able to help improve the prediction of a stock markedly.

Original languageEnglish
Title of host publicationEMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages1139-1145
Number of pages7
ISBN (Electronic)9781937284961
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 - Doha, Qatar
Duration: 25 Oct 201429 Oct 2014

Publication series

NameEMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

Conference2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014
Country/TerritoryQatar
CityDoha
Period25/10/1429/10/14

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Information Systems

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