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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/8229
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dc.contributor.authorSharma, Yashvardhan-
dc.date.accessioned2023-01-02T11:09:06Z-
dc.date.available2023-01-02T11:09:06Z-
dc.date.issued2019-
dc.identifier.urihttps://ieeexplore.ieee.org/document/8776609-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8229-
dc.description.abstractThis paper deals with the impediment of identifying sarcasm in social media text which can be used to improve sentiment analysis technique. After thorough analysis, some features were identified which could help in recognition of sarcasm. In state of art, features have been extracted from the data set which embraced standalone sentences. Proposed algorithm analyzes the impact of these features and a combination of them on the review data set in which reviews had three or more sentences, so that context of sentence is also taken into consideration by the machine before classifying a review.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectComputer Scienceen_US
dc.subjectSarcasm Detectionen_US
dc.subjectSentiment Analysisen_US
dc.subjectMachine Learningen_US
dc.subjectNatural Language Processingen_US
dc.titleFAID: Feature Aftermath for Irony Discernmenten_US
dc.typeArticleen_US
Appears in Collections:Department of Computer Science and Information Systems

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