Examining post COVID-19 tourist concerns using sentiment analysis and topic modeling

The COVID-19 pandemic has had a destructive effect on the tourism sector, especially on tourists’ fears and risk perceptions, and is likely to have a lasting impact on their intention to travel. Governments and businesses worldwide looking to revive and revamp their tourism sector, therefore, must first develop a critical understanding of tourist concerns starting from the dreaming/planning phase to booking, travel, stay, and experiencing. This formed the motivation of this study, which empirically examines the tourist sentiments and concerns across the tourism supply chain. Natural Language Processing (NLP) using sentiment analysis and Latent Dirichlet Allocation (LDA) approach was applied to analyze the semi-structured survey data collected from 72 respondents. Practitioners and policymakers could use the study findings to enable various support mechanisms for restoring tourist confidence and help them adjust to the ’new normal.’

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