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dc.contributor.authorGoonewardena, IO
dc.contributor.authorKalansooriya, Pradeep
dc.date.accessioned2020-12-31T22:22:45Z
dc.date.available2020-12-31T22:22:45Z
dc.date.issued2020
dc.identifier.urihttp://ir.kdu.ac.lk/handle/345/2999
dc.descriptionArticle Full Texten_US
dc.description.abstractEmotional intelligence is the ability to understand changing states of emotion, it is an important aspect of human interaction. With upcoming developments emotion identification is an important aspect in HCI. Ideally if a computer can identify a human’s emotions and respond to it accordingly human computer interactions would be much more natural and more convenient. But even from a human’s perspective emotions are hard to identify and track, hence for a computer to identify accurate emotions can be challenging. Nonetheless there exists few methods to classify and label emotions into categories. Hence this research is an analysis of methods used to classify emotions. Discussing the strengths and weaknesses in communication cues such as facial expression classifiers, gesture movements, acoustic emotion classifiers and emotion mining in text. It argues that there exists an increment of accuracy when two or more systems are paired to extract the features in different situations. Hence results show that, while each model has its advantages and disadvantages, when integrated to classify, it gives better, more accurate prediction and improved results. Additionally, this paper mentions some of the practical issues that exist when it comes to emotion recognition and HCI. Furthermore, it is identified that emotion identification via text is a research area which holds great potential and among many approaches hand crafted models with the use of machine learning gives the best results. Finally, it proposes a solution, a mobile application for emotional support using emotion identification via text messages.en_US
dc.language.isoenen_US
dc.subjectModulesen_US
dc.subjectUnimodalen_US
dc.subjectBimodalen_US
dc.subjectMultimodalen_US
dc.subjectEmotion miningen_US
dc.titleAnalysis on Emotion Classification Methodsen_US
dc.typeArticle Full Texten_US
dc.identifier.journalKDU-IRC-2020en_US
dc.identifier.pgnos493-505en_US


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