On the automatic learning of sentiment lexicons

Aliaksei Severyn, Alessandro Moschitti

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

15 Citations (Scopus)

Abstract

This paper describes a simple and principled approach to automatically construct sentiment lexicons using distant supervision. We induce the sentiment association scores for the lexicon items from a model trained on a weakly supervised corpora. Our empirical findings show that features extracted from such a machine-learned lexicon outperform models using manual or other automatically constructed sentiment lexicons. Finally, our system achieves the state-of-the-art in Twitter Sentiment Analysis tasks from Semeval-2013 and ranks 2nd best in Semeval-2014 according to the average rank.

Original languageEnglish
Title of host publicationNAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages1397-1402
Number of pages6
ISBN (Print)9781941643495
Publication statusPublished - 2015
EventConference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015 - Denver, United States
Duration: 31 May 20155 Jun 2015

Other

OtherConference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015
CountryUnited States
CityDenver
Period31/5/155/6/15

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ASJC Scopus subject areas

  • Computer Science Applications
  • Language and Linguistics
  • Linguistics and Language

Cite this

Severyn, A., & Moschitti, A. (2015). On the automatic learning of sentiment lexicons. In NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 1397-1402). Association for Computational Linguistics (ACL).