Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2359
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dc.contributor.authorBharti, S K-
dc.contributor.authorBabu, K S-
dc.contributor.authorJena, S K-
dc.date.accessioned2015-09-10T04:19:39Z-
dc.date.available2015-09-10T04:19:39Z-
dc.date.issued2015-08-
dc.identifier.citationThe 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015), Paris, France, 25-28 August 2015en_US
dc.identifier.urihttp://hdl.handle.net/2080/2359-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractSentiment Analysis is a technique to identify people’s opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics, product, organizations, services etc. Sarcasm is a special kind of sentiment that comprise of words which mean the opposite of what you really want to say (especially in order to insult or wit someone, to show irritation, or to be funny). People often expressed it verbally through the use of heavy tonal stress and certain gestural clues like rolling of the eyes. These tonal and gestural clues are obviously not available for expressing sarcasm in text, making its detection reliant upon other factors. In this paper, two approaches to detect sarcasm in the text of Twitter data were proposed. The first is a parsing-based lexicon generation algorithm (PBLGA) and the second was to detect sarcasm based on the occurrence of the interjection word. The combination of two approaches is also shown and compared with the existing state-of-the-art approach to detect sarcasm. First approach attains a 0.89, 0.81 and 0.84 precision, recall and f-􀀀 score respectively. Second approach attains 0.85, 0.96 and 0.90 precision, recall and f -􀀀 score respectively in tweets with sarcastic hashtag.en_US
dc.language.isoenen_US
dc.subjectNatural language processingen_US
dc.subjectOpinion miningen_US
dc.subjectParsingen_US
dc.subjectSarcasmen_US
dc.subjectSentimenten_US
dc.subjectTweetsen_US
dc.titleParsing-based Sarcasm Sentiment Recognition in Twitter Dataen_US
dc.typeArticleen_US
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