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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15665
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dc.contributor.authorSharma R.-
dc.contributor.authorGupta M.-
dc.contributor.authorAgarwal A.-
dc.contributor.authorBhattacharyya P.-
dc.date.accessioned2020-12-02T11:41:29Z-
dc.date.available2020-12-02T11:41:29Z-
dc.date.issued2015-
dc.identifier.citationProceedings of EMNLP 2015: Conference on Empirical Methods in Natural Language Processing, (2015), 2520- 2526-
dc.identifier.isbn9781941643327-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/15665-
dc.description.abstractFor fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compare adjectives that share a common semantic property. In this paper, we present a semi-supervised approach to assign intensity levels to adjectives, viz. high, medium and low, where adjectives are compared when they belong to the same semantic category. For example, in the semantic category of EXPERTISE, expert, experienced and familiar are respectively of level high, medium and low. We obtain an overall accuracy of 77% for intensity assignment. We show the significance of considering intensity information of adjectives in predicting star-rating of reviews. Our intensity based prediction system results in an accuracy of 59% for a 5-star rated movie review corpus. © 2015 Association for Computational Linguistics.-
dc.description.sponsorshipBaidu;Bloomberg;et al.;facebook;Google;Linkedin-
dc.language.isoen_US-
dc.publisherAssociation for Computational Linguistics (ACL)-
dc.relation.ispartofProceedings of EMNLP 2015: Conference on Empirical Methods in Natural Language Processing-
dc.subjectComputational linguistics-
dc.subjectData mining-
dc.subjectSemantics-
dc.subjectIntensity information-
dc.subjectIntensity levels-
dc.subjectOverall accuracies-
dc.subjectPrediction systems-
dc.subjectSemantic category-
dc.subjectSemantic properties-
dc.subjectSemi-supervised-
dc.subjectSentiment analysis-
dc.subjectNatural language processing systems-
dc.titleAdjective intensity and sentiment analysis-
dc.typeConference Paper-
dc.scopusid55582575200-
dc.scopusid57214001483-
dc.scopusid57213411861-
dc.scopusid7101803108-
dc.affiliationSharma, R., Dept. of Computer Science and Engineering, IIT Bombay, Mumbai, India-
dc.affiliationGupta, M., Dept. of Computer Science and Engineering, IIT Bombay, Mumbai, India-
dc.affiliationAgarwal, A., Dept. of Computer Science and Engineering, IIT Bombay, Mumbai, India-
dc.affiliationBhattacharyya, P., Dept. of Computer Science and Engineering, IIT Bombay, Mumbai, India-
dc.identifier.conferencedetailsConference on Empirical Methods in Natural Language Processing, EMNLP 2015, 17-21 September 2015-
Appears in Collections:Conference Publications [CS]

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