Chinese Tourist Motivations for Hokkaido, Japan: A Hybrid Approach Using Transformer Models and Statistical Methods

Zhenzhen Liu (), Juuso Eronen (), Fumito Masui and Michal Ptaszynski
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Zhenzhen Liu: Department of Computer Science, Kitami Institute of Technology, Kitami 090-8507, Hokkaido, Japan
Juuso Eronen: Department of Administrative Studies, Prefectural University of Kumamoto, Kumamoto 862-0920, Kyushu, Japan
Fumito Masui: Department of Computer Science, Kitami Institute of Technology, Kitami 090-8507, Hokkaido, Japan
Michal Ptaszynski: Department of Computer Science, Kitami Institute of Technology, Kitami 090-8507, Hokkaido, Japan

Tourism and Hospitality, 2025, vol. 6, issue 3, 1-23

Abstract: The COVID-19 pandemic severely impacted Japan’s inbound tourism, but recent recovery trends highlight the growing importance of Chinese tourists. Understanding their motivations is crucial for revitalizing the industry. Building on our previous framework, this study applies Transformer-based natural language processing (NLP) models and principal component analysis (PCA) to analyze large-scale user-generated content (UGC) and identify key motivational factors influencing Chinese tourists’ visits to Hokkaido. Traditional survey-based approaches to tourism motivation research often suffer from response biases and small sample sizes. In contrast, we leverage a pre-trained Transformer model, RoBERTa, to score motivational factors like self-expansion, excitement, and cultural observation. PCA is subsequently used to extract the most significant factors across different destinations. Findings indicate that Chinese tourists are primarily drawn to Hokkaido’s natural scenery and cultural experiences, and the differences in these factors by season. While the model effectively aligns with manual scoring, it shows limitations in capturing more abstract motivations such as excitement and self-expansion. This research advances tourism analytics by applying AI-driven methodologies, offering practical insights for destination marketing and management. Future work can extend this approach to other regions and cross-cultural contexts, further enhancing AI’s role in understanding evolving traveler preferences.

Keywords: tourism motivation; transformers; natural language processing; principal component analysis; tourist behaviour (search for similar items in EconPapers)
JEL-codes: Z3 Z30 Z31 Z32 Z33 Z38 (search for similar items in EconPapers)
Date: 2025
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