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Sentiment composition of words with opposing polarities

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.18653/v1/N16-1128
AuthorSearch for: 1; Search for: 1
Name affiliation
  1. National Research Council of Canada. Information and Communication Technologies
FormatText, Article
Conference2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2016, San Diego, California, USA
Abstract
In this paper, we explore sentiment composition in phrases that have at least one positive and at least one negative word—phrases like happy accident and best winter break. We compiled a dataset of such opposing polarity phrases and manually annotated them with real-valued scores of sentiment association. Using this dataset, we analyze the linguistic patterns present in opposing polarity phrases. Finally, we apply several unsupervised and supervised techniques of sentiment composition to determine their efficacy on this dataset. Our best system, which incorporates information from the phrase’s constituents, their parts of speech, their sentiment association scores, and their embedding vectors, obtains an accuracy of over 80% on the opposing polarity phrases.
Publication date
PublisherAssociation for Computational Linguistics
In
  • Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: 1102–1108.
LanguageEnglish
Peer reviewedYes
NPARC number23001910
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Record identifier94d1e786-a373-44e9-b816-b75e2c157f59
Record created2017-05-24
Record modified2020-03-16
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