Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning.

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: COVID-19 and current political tensions.

Factors affecting worriedness: A study of the COVID-19 pandemic in Japan.

The global effect of COVID-19 is no longer simply a public health issue; it is causing an economic crisis that has a significant impact on the job market and people's lives. The disease has led to 43% of businesses temporarily closing, and almost all these closures are due to COVID-19. Organizations that have temporarily suspended their activities have pointed mainly to a decline in demand and employee health issues as the reasons for closure. In emergency and disaster management, perception often helps shape personality and how people act in certain situations. This study aims to examine personal risk perception of COVID-19 from many viewpoints and whether it affects motivation with regard to improving personal preparedness. We collected data from three major Japanese cities through a questionnaire survey and analyzed the results of the survey through factor analysis and multiple regression analysis by using the Partial Least Square Structural Equation Modeling (PLS-SEM). The three study areas include (1) the most damaged regions from the 2011 Great East Japan earthquake and tsunami, (2) the capital city and surrounding areas of Tokyo, and (3) Kumamoto, which has recently experienced an earthquake. The findings show a correlation between the nature of the information received during COVID-19 and worriedness and the necessity for adequate information. The expected benefit of this study is to provide guidelines for the government or organizations to make a suitable emergency management plan based on pertinent factors for future pandemics.