Combining social cognitive theories with linguistic features for multi-genre sentiment analysis

Hao Li, Yu Chen, Heng Ji, Smaranda Muresan, Dequan Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the rapid development of social media and social networks, spontaneously user generated content like tweets and forum posts have become important materials for tracking people's opinions and sentiments online. In this paper we investigate the limitations of traditional linguistic-based approaches to sentiment analysis when applied to these informal genres. Inspired by various social cognitive theories, we combine local linguistic features and global social evidence in a propagation scheme to improve sentiment analysis results. Without using any additional labeled data, this new approach obtains significant improvement (up to 12% higher accuracy) for various genres in the domain of presidential election.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th Pacific Asia Conference on Language, Information and Computation, PACLIC 2012
Pages127-136
Number of pages10
StatePublished - 2012
Externally publishedYes
Event26th Pacific Asia Conference on Language, Information and Computation, PACLIC 2012 - Bali, Indonesia
Duration: Nov 7 2012Nov 7 2012

Publication series

NameProceedings of the 26th Pacific Asia Conference on Language, Information and Computation, PACLIC 2012

Conference

Conference26th Pacific Asia Conference on Language, Information and Computation, PACLIC 2012
Country/TerritoryIndonesia
CityBali
Period11/7/1211/7/12

ASJC Scopus subject areas

  • Information Systems
  • Software

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