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In the year 2020, SARS-CoV-2, the virus behind the coronavirus disease (COVID-19) pandemic, affected many lives and businesses worldwide. COVID-19, which originated in Wuhan City, China, at the end of December 2019, spread over the entire world in approximately four months. By October 2020, approximately 20 million people were infected and millions had died from this disease. Many health organizations such as the World Health Organization and Centers for Disease Control and Prevention made COVID-19 their primary focus. Many industries, especially, the tourism industry, were affected by the pandemic as many flight and hotel reservations were canceled. Thailand, a country considered one of the world’s most popular tourist destinations, suffered much losses because of this pandemic. Many events and travel bookings were canceled and/or postponed. Many people expressed their views and emotions related to this situation over social media, which is considered a powerful media for spreading news and information. In this research, the views of people who were planning to travel to Bangkok, the capital city and most popular destination in Thailand, were retrieved from Twitter for the dates between April 3 and 30, 2020, the period during which the country underwent nationwide lockdown. Sentiment analysis was performed using the support vector machine algorithm. The results showed 71.03% classification accuracy based on three sentiment classifications: positive, negative, and neutral. This study could thus provide an insight into travelers’ opinions and sentiments related to the tourism business. Based on the significant terms in each sentiment extracted, strengths and weaknesses of each tourism issue could be obtained, which could be used for making recommendations to the related tourism organizations.

The Severe Acute Respiratory Syndrome Coronavirus 2 is a virus causing the COVID-19 pandemic around the world. The World Health Organization (WHO) raised it to the highest level of global alert. The English, Chinese, and Japanese language Twitter data related to this disease during the first period after the WHO started releasing the situation reports were collected and compared with the tweet trends. This study also used quantitative text analysis to extract and analyze the co-occurrence network of English tweets. The findings show that trends and public concerns in social media are related to the breaking news and global trends such as the confirmed cases, the reported death tolls, the quarantined cruise news, the informer, etc.

When considering a tsunami disaster, many researchers have considered the tsunami's flow depth and velocity as the primary contributors to the building damage. Additionally, the majority of these studies have used the maximum value as the measure of each of these two factors. However, building damage may not occur when the maximum flow depth and the maximum flow velocity of the tsunami are reached. This study addressed two objectives based on the 2011 Great East Japan Earthquake and Tsunami. Firstly, to find out whether the maximum values of the flow depth and flow velocity are the same as their critical values and, secondly, to verify which combination of the parameters is the best predictor of the building damage level. The data from 18,000 buildings in Ishinomaki City, Japan, with the cooperation of the Japanese joint survey team, were analyzed using the decision tree related algorithms. The critical variables were the simulated data at the time when the buildings collapsed. The analysis showed the accuracy of the prediction based on the group of variables. Finally, the findings showed that the combination of the critical flow depth and maximum flow velocity provided the highest accuracy for classifying the level of building damage.

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