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dc.contributor.authorBinali, Haji
dc.contributor.authorWu, Chen
dc.contributor.authorPotdar, Vidyasagar
dc.contributor.editorLeila Ismail
dc.contributor.editorElizabeth Chang
dc.contributor.editorAchim P Karduck
dc.identifier.citationBinali, Haji and Wu, Chen and Potdar, Vidyasagar. 2010. Computational Approaches for Emotion Detection in Text, in Ismail, L. and Chang, E. and Karduck, A.P. (ed), IEEE international conference on digital ecosystems and technologies (DEST 2010), Apr 12 2010, pp. 172-177. Dubai, United Arab Emirates: IEEE.

Emotions are part and parcel of human life and among other things, highly influence decision making. Computers have been used for decision making for quite some time now but have traditionally relied on factual information. Recently, interest has been growing among researchers to find ways of detecting subjective information used in blogs and other online social media. This paper looks at emotion theories that provide a basis for emotion models. It shows how these models have been used by discussing computational approaches to emotion detection. The emotion detection architecture that we propose consists of two components, knowledge based approach and learning systems approach. We present a prototype based on an architecture that we have proposed and demonstrate some of the challenges involved in detecting emotions from text.

dc.subjectText classification
dc.subjectSentiment analysis
dc.subjectEmotion detection
dc.subjectEmotion models
dc.titleComputational Approaches for Emotion Detection in Text
dc.typeConference Paper
dcterms.source.titleProceedings of the IEEE international conference on digital ecosystems and technologies (DEST 2010)
dcterms.source.seriesProceedings of the IEEE international conference on digital ecosystems and technologies (DEST 2010)
dcterms.source.conferenceIEEE international conference on digital ecosystems and technologies (DEST 2010)
dcterms.source.conference-start-dateApr 12 2010
dcterms.source.conferencelocationDubai, United Arab Emirates
dcterms.source.placeUnited Arab Emirates

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curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusOpen access

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