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

Online 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.

Paper Details

Date Published: 31 December 2019
PDF: 5 pages
Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 113840V (31 December 2019); doi: 10.1117/12.2559609
Show Author Affiliations
Warakorn Boonpetch, King Mongkut's Institute of Technology Ladkrabang (Thailand)
Orachat Chitsobhuk, King Mongkut's Institute of Technology Ladkrabang (Thailand)


Published in SPIE Proceedings Vol. 11384:
Eleventh International Conference on Signal Processing Systems
Kezhi Mao, Editor(s)

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