A comparative study on the perception of integrated resort in Korea before and after COVID-19 using social big data

https://doi.org/10.55214/25768484.v8i6.2271

Authors

  • Jang Heon Han Department of Tourism Administration, Yong in University, Gyeonggi-do 17092, Korea
  • Kab Soo An Department of Hotel, Casino and Tourism, Han Seo University, Chungcheongnam-do 319662, Korea

The study was conducted to compare and analyze perceptions of integrated resorts before and after the outbreak of COVID-19 and to explore ways to revitalize the operation of integrated resorts that can be utilized in the post-Corona era. Keywords highly related to integrated resorts were collected from major social media and SNS channels in Korea, such as Naver, Daum, and Google. Unstructured big data analysis research was conducted using social network analysis and semantic network analysis, which are major methodologies for social big data. The keyword data collection period was divided into the time before and after the outbreak of COVID-19. A total of 7,933 keywords were collected before the outbreak, and 7,395 were collected during the period after the outbreak. The study included a total of 100 keywords, with 50 from each period. The research results showed that, first, before the outbreak of COVID-19, the keywords ‘Casino’, ‘Yeongjong-do’, ‘Hotel’, ‘Dream Tower’, ‘Incheon’, ‘Development’, ‘Domestic’, and ‘Business’ appeared with high frequency. After the outbreak, the keywords ‘Jeju’, ‘Resort’, ‘Dream Tower’, ‘Hotel’, ‘Inspire’, and ‘Business’ showed high frequencies of appearance. Second, CONCOR analysis confirmed the presence of seven keyword clusters before and after the outbreak of COVID-19. Based on the results of this study, various implications for integrated resorts that can be utilized in the post-Corona era suggested, along with future directions for integrated resort research.

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How to Cite

Han, J. H. ., & An, K. S. . (2024). A comparative study on the perception of integrated resort in Korea before and after COVID-19 using social big data. Edelweiss Applied Science and Technology, 8(6), 1538–1553. https://doi.org/10.55214/25768484.v8i6.2271

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Published

2024-10-11