Affective topic model for social emotion detection

Y. Rao, Qing Li, L. Wenyin, Q. Wu, X. Quan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

64 Citations (Scopus)

Abstract

The rapid development of social media services has been a great boon for the communication of emotions through blogs, microblogs/tweets, instant-messaging tools, news portals, and so forth. This paper is concerned with the detection of emotions evoked in a reader by social media. Compared to classical sentiment analysis conducted from the writer's perspective, analysis from the reader's perspective can be more meaningful when applied to social media. We propose an affective topic model with the intention to bridge the gap between social media materials and a reader's emotions by introducing an intermediate layer. The proposed model can be used to classify the social emotions of unlabeled documents and to generate a social emotion lexicon. Extensive evaluations using real-world data validate the effectiveness of the proposed model for both these applications. © 2014 Elsevier Ltd.
Original languageEnglish
Pages (from-to)29-37
Number of pages9
JournalNeural Networks
Volume58
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Affective topic model
  • Sentiment classification
  • Social emotion detection
  • Social emotion lexicon

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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