Joint modeling of news reader's and comment writer's emotions

Huanhuan Liu, Shoushan Li, Guodong Zhou, Chu-ren Huang, Peifeng Li

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

25 Citations (Scopus)


Emotion classification can be generally done from both the writer's and reader's perspectives. In this study, we find that two foundational tasks in emotion classification, i.e., reader's emotion classification on the news and writer's emotion classification on the comments, are strongly related to each other in terms of coarse-grained emotion categories, i.e., negative and positive. On the basis, we propose a respective way to jointly model these two tasks. In particular, a co-training algorithm is proposed to improve semi-supervised learning of the two tasks. Experimental evaluation shows the effectiveness of our joint modeling approach.
Original languageEnglish
Title of host publicationShort Papers
PublisherAssociation for Computational Linguistics (ACL)
Number of pages5
ISBN (Print)9781937284510
Publication statusPublished - 1 Jan 2013
Event51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
Duration: 4 Aug 20139 Aug 2013


Conference51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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