Title of Paper

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

Author Bios (50 Words for each Author)

Dongdong Wu is a PhD candidate at College of Tourism and Service Management, Nankai University, China. He is also a short-term visiting student at the Department of Industrial and Systems Engineering, Lamar University. His research focuses on sustainable tourism, text mining, tourism firm management, and event study. Email: dwu@mail.nankai.edu.cn.

Hui Li is a Young Chang-Jiang professor and Associate Dean at College of Tourism and Service Management, Nankai University, China. His research focuses on tourism big data mining and prediction, tourism firm management and governance. Email: lihuihit@gmail.com.

Yueqing Li is an associate professor at Department of Industrial and Systems Engineering, Lamar University, USA. His research focuses on date analytics, human factors & ergonomics, human-computer interaction, and neuroergonomics. Email: yueqing.li@lamar.edu.

Yuhong Wang is a full professor majoring in Management Science and Engineering at School of Business, Jiangnan University, China. His research focuses on big data management and application, uncertainty prediction and decision making, and grey system theory. Email: wyh2003@gmail.com.

Abstract (150 Words)

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.

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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.