A New Approach to Extracting Tourism Focus Points from Chinese Inbound Tourist Reviews after COVID-19

Zhenzhen Liu (), Fumito Masui, Juuso Eronen, Shun Terashita and Michal Ptaszynski
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Zhenzhen Liu: Doctoral Course in Manufacturing Engineering, Kitami Institute of Technology, Kitami 090-8507, Japan
Fumito Masui: Department of Computer Science, Kitami Institute of Technology, Kitami 090-8507, Japan
Juuso Eronen: Department of Administrative Studies, Prefectural University of Kumamoto, Kumamoto 862-0920, Japan
Shun Terashita: Master Course in Computer Science, Kitami Institute of Technology, Kitami 090-8507, Japan
Michal Ptaszynski: Department of Computer Science, Kitami Institute of Technology, Kitami 090-8507, Japan

Sustainability, 2023, vol. 15, issue 11, 1-24

Abstract: The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan’s tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists’ reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

Keywords: inbound tourist reviews; focus points; motivation factors; n-gram patterns; PCA (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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