Sentiment topic models for social emotion mining

Y. Rao, Qing Li, X. Mao, L. Wenyin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

124 Citations (Scopus)


The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers' emotions evoked by social media materials. Compared to the classical sentiment analysis from writers' perspective, sentiment analysis of readers is sometimes more meaningful in social media. We propose two sentiment topic models to associate latent topics with evoked emotions of readers. The first model which is an extension of the existing Supervised Topic Model, generates a set of topics from words firstly, followed by sampling emotions from each topic. The second model generates topics from social emotions directly. Both models can be applied to social emotion classification and generate social emotion lexicons. Evaluation on social emotion classification verifies the effectiveness of the proposed models. The generated social emotion lexicon samples further show that our models can discover meaningful latent topics exhibiting emotion focus. © 2013 Published by Elsevier Inc.
Original languageEnglish
Pages (from-to)90-100
Number of pages11
JournalInformation Sciences
Publication statusPublished - 10 May 2014
Externally publishedYes


  • Sentiment topic model
  • Social emotion classification
  • Social emotion lexicon
  • Social emotion mining

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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