dc.description.abstract | The coronavirus disease 2019 (COVID-19) pandemic has severely affected Thailand's economy, which relies
heavily on tourism. In this study, we labeled the sentiment and intention classes of English-language tweets
related to tourism in Bangkok, Chiang Mai, and Phuket. Then, the accuracy of three machine learning algorithms
(decision tree, random forest, and support vector machine) in predicting the sentiments and intentions of the
tweets was investigated. The support vector machine algorithm provided the best results for sentiment analysis,
with a maximum accuracy of 77.4%. In the intention analysis, the random forest algorithm achieved an accuracy
of 95.4%. In a subsequent preliminary qualitative content analysis, the top 10 words found in each sentiment and
intention class were gathered to provide insights and suggestions to help increase tourism in Thailand. The results
of this study suggest that to help restore tourism in Thailand, tourist destinations, natural attractions, restaurants,
and nightlife should be promoted. In addition, the two main concerns of tourists to Thailand should be addressed: | vi |