Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151125
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dc.contributor.authorXing, Frank Z.en_US
dc.contributor.authorPallucchini, Filippoen_US
dc.contributor.authorCambria, Eriken_US
dc.date.accessioned2021-06-24T10:11:52Z-
dc.date.available2021-06-24T10:11:52Z-
dc.date.issued2019-
dc.identifier.citationXing, F. Z., Pallucchini, F. & Cambria, E. (2019). Cognitive-inspired domain adaptation of sentiment lexicons. Information Processing and Management, 56(3), 554-564. https://dx.doi.org/10.1016/j.ipm.2018.11.002en_US
dc.identifier.issn0306-4573en_US
dc.identifier.other0000-0002-5751-3937-
dc.identifier.other0000-0002-3030-1280-
dc.identifier.urihttps://hdl.handle.net/10356/151125-
dc.description.abstractSentiment lexicons are essential tools for polarity classification and opinion mining. In contrast to machine learning methods that only leverage text features or raw text for sentiment analysis, methods that use sentiment lexicons embrace higher interpretability. Although a number of domain-specific sentiment lexicons are made available, it is impractical to build an ex ante lexicon that fully reflects the characteristics of the language usage in endless domains. In this article, we propose a novel approach to simultaneously train a vanilla sentiment classifier and adapt word polarities to the target domain. Specifically, we sequentially track the wrongly predicted sentences and use them as the supervision instead of addressing the gold standard as a whole to emulate the life-long cognitive process of lexicon learning. An exploration-exploitation mechanism is designed to trade off between searching for new sentiment words and updating the polarity score of one word. Experimental results on several popular datasets show that our approach significantly improves the sentiment classification performance for a variety of domains by means of improving the quality of sentiment lexicons. Case-studies also illustrate how polarity scores of the same words are discovered for different domains.en_US
dc.language.isoenen_US
dc.relation.ispartofInformation Processing and Managementen_US
dc.rights© 2018 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleCognitive-inspired domain adaptation of sentiment lexiconsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1016/j.ipm.2018.11.002-
dc.identifier.scopus2-s2.0-85059608591-
dc.identifier.issue3en_US
dc.identifier.volume56en_US
dc.identifier.spage554en_US
dc.identifier.epage564en_US
dc.subject.keywordsSentiment Lexiconen_US
dc.subject.keywordsDomain Adaptationen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:SCSE Journal Articles

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