Boonyanit MathayomchanKunwadee SripanidkulchaiChulalongkorn UniversityMahidol University2020-01-272020-01-272019-07-01JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence. (2019), 260-2652-s2.0-85074223444https://repository.li.mahidol.ac.th/handle/20.500.14594/50630© 2019 IEEE. Well-known tourist attractions receive reviews in various languages. Google Maps has a reviewing system with translation service. The translated reviews may not perfectly represent the original review, but may still capture valuable information. This paper studies the effectiveness of nonusing translated reviews in sentiment analysis for tourist attractions in Phuket, Thailand. Reviews for beaches in Phuket and nearby islands are collected. The reviews are separated into two groups, reviews originally written in English and non-English. Rather than discarding non-English reviews or constructing models for each origin language, we experiment with different ways of utilizing the non-English reviews by translating them into English. Results show that models constructed using the reviews originally written in English are also effective for translated reviews. This can significantly reduce the effort to understand the sentiment of reviews written in various languages by training only one model and applying it to text translated from any language.Mahidol UniversityComputer ScienceDecision SciencesConference PaperSCOPUS10.1109/JCSSE.2019.8864150