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

Dongdong Wu (), Hui Li (), Yueqing Li () and Yuhong Wang ()
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Dongdong Wu: College of Tourism and Service Management, Nankai University
Hui Li: College of Tourism and Service Management, Nankai University
Yueqing Li: Department of Industrial and Systems Engineering, Lamar University
Yuhong Wang: School of Business, Jiangnan University

Chapter Chapter 14 in COVID-19, Tourist Destinations and Prospects for Recovery, 2023, pp 261-276 from Springer

Abstract: Abstract The primary purpose of this chapter is to get to know the public attitude towards sustainable tourism after COVID-19 and its polarity or emotion. Using Twitter Archiving Google Sheet, 6718 tweets were collected from July 11 to August 10, 2021, with the hashtags #covid19 and #tourism, #sustainabletourism or #ecotourism or #responsibletourism. Tableau and Gephi were used to visualise and aggregate the social media network. Using R Studio, the word frequency, association and sentiment analysis were carried out. The main findings are as follows: (1) retweets take most of all data; (2) media accounts are more visible and active than individual ones in the community network; (3) the “trust” emotion and “anticipation” emotion are dominant in the tweets. Besides, this chapter also tried to use related social behaviour theories to explain the observed social media user behaviours. Practical implications have also been provided to dissolve people’s psychological and emotional problems and enhance people’s confidence in tourism recovery.

Keywords: Post-COVID-19; Sustainable tourism; Text mining; Social media network; Twitter; Sentiment analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-22257-3_14

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DOI: 10.1007/978-3-031-22257-3_14

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