The effect of negators, modals, and degree adverbs on sentiment composition

DOIResolve DOI: http://doi.org/10.18653/v1/W16-0410
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TypeArticle
Proceedings titleProceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Conference7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, June 2016, San Diego, California
Pages4352
AbstractNegators, modals, and degree adverbs can significantly affect the sentiment of the words they modify. Often, their impact is modeled with simple heuristics; although, recent work has shown that such heuristics do not capture the true sentiment of multi-word phrases. We created a dataset of phrases that include various negators, modals, and degree adverbs, as well as their combinations. Both the phrases and their constituent content words were annotated with real-valued scores of sentiment association. Using phrasal terms in the created dataset, we analyze the impact of individual modifiers and the average effect of the groups of modifiers on overall sentiment. We find that the effect of modifiers varies substantially among the members of the same group. Furthermore , each individual modifier can affect sentiment words in different ways. Therefore, solutions based on statistical learning seem more promising than fixed hand-crafted rules on the task of automatic sentiment prediction.
Publication date
PublisherAssociation for Computational Linguistics
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23001908
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Record identifier2c709c7d-e2f8-4941-93b5-0c65f4c978f1
Record created2017-05-24
Record modified2017-05-24
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