Text Mining Tweets on Post COVID-19 Sustainable Tourism Through Social Media Network and Sentiment Analysis

While the impact of the COVID-19 pandemic has been widely studied, relatively fewer attentions about the social media network and sentimental reaction have been available, especially on post COVID-19 sustainable tourism. Using Twitter Archiving Google Sheet, tweets were collected with the hashtags: #covid19 and #tourism; #sustainabletourism or #ecotourism or #responsibletourism. Tableau and Gephi are used to visualize and aggregate the social media network. By using R Studio, the word frequency, association and sentiment analysis are carried out. The social media network and sentiment analysis provides new discoveries about post COVID-19 sustainable tourism tweets. Text information from social media can be used to supplement data sources and provides direct insights into public perceptions of post COVID-19 sustainable tourism. According to the analysis results, this paper also tries to use related social behavior theories to explain the observed social media user behaviors