Cross domain sentiment classification of Thai reviews using co-train model

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dc.contributor.authorBoonpetch, Warakorn
dc.contributor.authorChitsobhuk, Orachat
dc.date.accessioned2026-08-06T10:22:51Z
dc.date.available2026-08-06T10:22:51Z
dc.date.issued2019-01-01
dc.description.abstractOnline reviews are significant sources of information, which is useful for supporting customer and entrepreneur decision in terms of product and service satisfaction analysis. Online reviews containing feedback from various domains makes it difficult to analyze and classify all comments at once. The proposed technique analyses the cross-domain Thai review data using a co-train machine learning model. The co-train model consists of multiple single domain specific models followed by refinement analysis for the final sentiment classification. This allows for full flexibility in training of each individual domain, which can lessen the limitation on training complexity due to simple training on single domain. The experiments have been conducted on Wongnai restaurant domain and IMDB movie domain data. Our co-train model can achieve the highest average accuracy of 86.10 percent for cross-domain sentiment classification with approximately 38 seconds processing time.
dc.identifier.citationProceedings of SPIE the International Society for Optical Engineering, 11384, 2019
dc.identifier.doi10.1117/12.2559609
dc.identifier.issn0277786X
dc.identifier.other2-s2.0-85078964612
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/9396
dc.sourceProceedings of SPIE the International Society for Optical Engineering
dc.subjectCross domain
dc.subjectMulti domain
dc.subjectNatural language processing
dc.subjectSentiment analysis
dc.subjectSentiment classification
dc.titleCross domain sentiment classification of Thai reviews using co-train model
dc.typeConference Paper

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