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Adaptive Recursive Neural Network for target-dependent Twitter sentiment classification

  • Li Dong
  • , Furu Wei
  • , Chuanqi Tan
  • , Duyu Tang
  • , Ming Zhou
  • , Ke Xu
  • Beihang University
  • Microsoft USA
  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose Adaptive Recursive Neural Network (AdaRNN) for target-dependent Twitter sentiment classification. AdaRNN adaptively propagates the sentiments of words to target depending on the context and syntactic relationships between them. It consists of more than one composition functions, and we model the adaptive sentiment propagations as distributions over these composition functions. The experimental studies illustrate that AdaRNN improves the baseline methods. Furthermore, we introduce a manually annotated dataset for target-dependent Twitter sentiment analysis.

Original languageEnglish
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages49-54
Number of pages6
ISBN (Print)9781937284732
DOIs
StatePublished - 2014
Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
Duration: 22 Jun 201427 Jun 2014

Publication series

Name52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference
Volume2

Conference

Conference52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
Country/TerritoryUnited States
CityBaltimore, MD
Period22/06/1427/06/14

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