Publication:
Sentiment classification of Sinhala content in social media

dc.contributor.authorJayasuriya, P
dc.contributor.authorEkanayake, S
dc.contributor.authorMunasinghe, R
dc.contributor.authorMunasinghe, B
dc.contributor.authorWeerasinghe, I
dc.contributor.authorThelijjagoda, S
dc.date.accessioned2022-04-25T10:28:07Z
dc.date.available2022-04-25T10:28:07Z
dc.date.issued2020-09-24
dc.description.abstractIn this study, we focus on the classification of Sinhala social media sentiments into positive and negative classes for a particular domain (sports). We have employed machine learning algorithms and lexicon-based sentiment classification methods. We also consider a hybrid approach by constructing an ensemble classifier in which we combine Machine Learning and Lexicon based methods. For individual methods, machine learning algorithms performed best in terms of accuracy. The ensemble classifier was able to improve performance further.en_US
dc.identifier.citationP. Jayasuriya, S. Ekanayake, R. Munasinghe, B. Kumarasinghe, I. Weerasinghe and S. Thelijjagoda, "Sentiment classification of Sinhala content in social media," 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE), 2020, pp. 136-141, doi: 10.1109/SCSE49731.2020.9313023.en_US
dc.identifier.doi10.1109/SCSE49731.2020.9313023en_US
dc.identifier.issn2613-8662
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/2064
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2020 International Research Conference on Smart Computing and Systems Engineering (SCSE);Pages 136-141
dc.subjectSentiment classificationen_US
dc.subjectSinhala contenten_US
dc.subjectsocial mediaen_US
dc.titleSentiment classification of Sinhala content in social mediaen_US
dc.typeArticleen_US
dspace.entity.typePublication

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