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dc.contributor.authorBandhakavi, Anil
dc.contributor.authorWiratunga, Nirmalie
dc.contributor.authorMassie, Stewart
dc.contributor.authorPadmanabhan, Deepak
dc.date.accessioned2016-11-11T15:09:24Z
dc.date.available2016-11-11T15:09:24Z
dc.date.issued2016-11-05en
dc.identifier.citationBANDHAKAVI, A., WIRATUNGA, N. and MASSIE, S. 2016. Emotion-corpus guided lexicons for sentiment analysis on Twitter. In Bramer, M. and Petridis, M. (eds.) 2016. Research and development in intelligent systems XXXIII: incorporating applications and innovations in intelligent systems XXIV: proceedings of the 36th SGAI nternational conference on innovative techniques and applications of artificial intelligence (AI-2016), 13-15 December 2016, Cambridge, UK. Cham, Switzerland: Springer [online], pages 71-86. Available from: https://doi.org/10.10007/978-3-319-47175-4_5en
dc.identifier.isbn9783319471747en
dc.identifier.isbn9783319471754en
dc.identifier.urihttp://hdl.handle.net/10059/1972
dc.description.abstractResearch in Psychology have proposed frameworks that map emotion concepts with sentiment concepts. In this paper we study this mapping from a computational modelling perspective with a view to establish the role of an emotion-rich corpus for lexicon-based sentiment analysis. We propose two different methods which harness an emotion-labelled corpus of tweets to learn world-level numerical quantification of sentiment strengths over a positive to negative spectrum. The proposed methods model the emotion corpus using a generative unigram mixture model (UMM), combined with the emotion-sentiment mapping proposed in Psychology [6] for automated generation of sentiment lexicons. Sentiment analsysis experiments on benchmark Twitter data sets confirm the equality of our proposed lexicons. Further a comparative analysis with standard sentiment lexicons suggest that the proposed lexicons lead to a significantly better performance in both sentimentclassification and sentiment intensity prediction tasks.en
dc.language.isoengen
dc.publisherSpringeren
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectKnowledge discovertyen
dc.subjectData miningen
dc.subjectSpeechen
dc.subjectNatural language interfacesen
dc.subjectMachine learningen
dc.subjectOntologiesen
dc.subjectSemantic weben
dc.titleEmotion-corpus guided lexicons for sentiment analysis on Twitter.en
dc.typeConference publicationsen
dc.publisher.urihttps://doi.org/10.1007/978-3-319-47175-4_5en
dcterms.dateAccepted2016-06-10en
refterms.accessExceptionNAen
refterms.dateDeposit2016-11-11en
refterms.dateEmbargoEnd2017-11-05en
refterms.dateFCA2017-11-05en
refterms.dateFCD2016-11-11en
refterms.dateFreeToDownload2017-11-05en
refterms.dateFreeToRead2017-11-05en
refterms.dateToSearch2017-11-05en
refterms.depositExceptionNAen
refterms.panelBen
refterms.technicalExceptionNAen
refterms.versionAMen
rioxxterms.publicationdate2016-11-05en
rioxxterms.typeConference Paper/Proceeding/Abstracten
rioxxterms.versionAMen


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