Influence of COVID-19 in Tourism: A Sentiment Analysis of Twitter Data

This study investigates the economic impacts of the COVID-19 pandemic on Thailand's tourism-driven economy by analyzing English-language tweets related to tourism from July to December 2020. Utilizing advanced machine learning models-Logistic Regression, Decision Tree Classifier, XGBoost Classifier, and the Pelican Optimization Algorithm-we aimed to understand the sentiments and intentions expressed in these tweets. The research analysis revealed high predictive accuracy, with Logistic Regression achieving 98%, Decision Tree Classifier 99%, XGBoost Classifier 96%, and Pelican Optimization Algorithm 94.5%. These results highlight the importance of addressing concerns related to COVID-19 and political tensions to support the recovery of Thailand's tourism sector. The research also highlights the critical role of information and communications technology (ICT) in managing and revitalizing the tourism industry, especially in the context of reduced consumer spending and travel restrictions. Social media platforms, particularly Twitter, were used to gauge public sentiment and inform recovery strategies. The proposed methodology included extensive data collection, cleaning, and natural language processing, followed by the application of various machine learning algorithms to analyze and interpret sentiment data. The findings offer actionable insights for enhancing tourism marketing strategies and improving the sector's resilience amid ongoing pandemic challenges.