Automated Evaluation of Tourism Motivation from Chinese Tourists in Japan using Transformers
DOI:
https://doi.org/10.51094/jxiv.1216Keywords:
Inbound tourism, tourism motivation, transformersAbstract
The COVID-19 pandemic caused a significant drop in tourism in 2020, threatening Japan's goal of attracting 60 million tourists annually by 2030. To aid recovery, we propose an automated method to identify tourist motivations by analyzing tourist spot reviews. While previous studies on tourism motivation have primarily relied on labor-intensive approaches like questionnaires and interviews, this research uses deep-learning to automatically assess tourism motivations, reducing time and effort. Our study focuses on Hokkaido, a popular destination for Chinese tourists, building on previous research that manually scored motivation factors in reviews and used PCA to quantify focus points (the strongest tourism motivation) of the tourist spots. We enhance this by using pre-trained transformer models, to automatically assess reviews based on seven motivation factors. The results show that RoBERTa effectively scores these factors, closely matching manual assessments while significantly reducing the required time and human effort. However, the model is less accurate in identifying motivations linked to personal experiences. This automated approach offers valuable insights into tourist preferences, with the potential to inform and optimize Japan's tourism strategies, playing a crucial role in the industry's recovery and helping to meet national tourism goals.
Conflicts of Interest Disclosure
The authors declare no conflict of interest related to this work.Downloads *Displays the aggregated results up to the previous day.
References
Alaei, A. R., Becken, S., and Stantic, B. (2019). Sentiment analysis in tourism: capitalizing on big data. Journal of travel research, 58(2):175–191.
Annika Aebli, M. V. and Taplin, R. (2022). A two-dimensional approach to travel motivation in the context of the covid-19 pandemic. Current Issues in Tourism, 25(1):60–75.
Cambria, E. and White, B. (2014). Jumping nlp curves: A review of natural language processing research [review article]. IEEE Computational Intelligence Magazine, 9(2):48–57.
Chen, Q., Liu, R., Jiang, Q., and Xu, S. (2024). Exploring cross-cultural disparities in tourists’ perceived images: a text mining and sentiment analysis study using lda and bert-bilstm models. Data Technologies and Applications.
Chi, N. T. K. and Phuong, V. H. (2022). Studying tourist intention on city tourism: the role of travel motivation. International Journal of Tourism Cities, 8(2):497–512.
Clark, K., Luong, M.-T., Le, Q. V., and Manning, C. D. (2020). Electra: Pre-training text encoders as discriminators rather than generators.
Cohen, E. (1972). Toward a sociology of international tourism. Social Research, 39(1):164–182.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. routledge.
Crompton, J. L. (1979). Motivations for pleasure vacation. Annals of Tourism Research, 6(4):408–424.
Dann, G. M. (1981). Tourist motivation an appraisal. Annals of Tourism Research, 8(2):187–219.
Devesa, M., Laguna, M., and Palacios, A. (2010). The role of motivation in visitor satisfaction: Empirical evidence in rural tourism. Tourism Management, 31(4):547–552.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Burstein, J., Doran, C., and Solorio,T., editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long andShort Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Dunn Ross, E. L. and Iso-Ahola, S. E. (1991). Sightseeing tourists’ motivation and satisfaction. Annals of Tourism Research, 18(2):226–237.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. http://www.deeplearningbook.org.
Gregoriades, A., Pampaka, M., Herodotou, H., and Christodoulou, E. (2023). Explaining tourist revisit intention using natural language processing and classification techniques. Journal of Big Data, 10(1):60.
Hawkins, D. M. (2004). The problem of overfitting. Journal of Chemical Information and Computer Sciences, 44(1):1–12. PMID: 14741005.
Hayashi, Y. and Fujihara, T. (2008). Sightseeing motives of japanese overseas tourists as a function of destination, tour type and age. The Japanese Journal of Experimental Social Psychology, 48(1):17–31.
Henseler, J., Ringle, C. M., and Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing, pages 277–319. Emerald Group Publishing Limited.
Hsu, C. and Huang, S. (2007). Travel motivation: A critical review of the concept’s development. Tourism management: Analysis, behaviour and strategy, pages 14–27.
Hu, M. and Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168–177.
Huang, S. and Hsu, C. H. (2009). Travel motivation: linking theory to practice. International journal of culture, tourism and hospitality research, 3(4):287–295.
Jiang, S., Scott, N., Tao, L., and Ding, P. (2020). Chinese tourists’ motivation and their relationship to cultural values. In Culture and Cultures in Tourism, pages 202–214. Routledge.
JNTO (2024). 2024 visitor arrivals and japanese overseas travelers. https://www.jnto.go.jp/statistics/data/_files/20240821_1530-1.pdf.
Koo, T. K. and Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine, 15(2):155–163.
Lin, P. M., Qiu Zhang, H., Gu, Q., and Peng, K.-L. (2017). To go or not to go: Travel constraints and attractiveness of travel affecting outbound chinese tourists to japan. Journal of Travel & Tourism Marketing, 34(9):1184–1197.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach.
Liu, Z., Masui, F., Eronen, J., Terashita, S., and Ptaszynski, M. (2023). A new approach to extracting tourism focus points from chinese inbound tourist reviews after covid-19. Sustainability, 15(11).
Mart´ın, C. A., Torres, J. M., Aguilar, R. M., and Diaz, S. (2018). Using deep learning to predict sentiments: case study in tourism. Complexity, 2018(1):7408431.
M.Carvache-Franco, W.Carvache-Franco, O.Carvache-Franco, Hern´andez-Lara, A. B., and Buele, C. V. (2020). Segmentation, motivation, and sociodemographic aspects of tourist demand in a coastal marine destination: a case study in manta (ecuador). Current Issues in Tourism, 23(10):1234–1247.
Mishra, R. K., Urolagin, S., Jothi, J. A. A., Neogi, A. S., and Nawaz, N. (2021). Deep learning-based sentiment analysis and topic modeling on tourism during covid-19 pandemic. Frontiers in Computer Science, 3.
Pang, B., Lee, L., et al. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in information retrieval, 2(1–2):1–135.
Park, D.-B. and Yoon, Y.-S. (2009). Segmentation by motivation in rural tourism: A korean case study. Tourism management, 30(1):99–108.
Pearce, P. (2012). The Ulysses factor: Evaluating visitors in tourist settings. Springer Science & Business Media.
Phillips, P., Zigan, K., Santos Silva, M. M., and Schegg, R. (2015). The interactive effects of online reviews on the determinants of swiss hotel performance: A neural network analysis. Tourism Management, 50:130–141.
Salim, E. and Ravanel, L. (2023). Last chance to see the ice: visitor motivation at montenvers-mer-de-glace, french alps. Tourism Geographies, 25(1):72–94.
Su, D. N., Nguyen, N. A. N., Nguyen, Q. N. T., and Tran, T. P. (2020). The link between travel motivation and satisfaction towards a heritage destination: The role of visitor engagement, visitor experience and heritage destination image. Tourism Management Perspectives, 34:100634.
The Asahi Shimbun (2024). Inbound tourists hit record high for first six months of year. https://www.asahi.com/ajw/articles/15353063.
The Japan Times (2024). Inbound tourism numbers hit record high, with japan set to achieve 2025 goal. https://www.japantimes.co.jp/news/2024/04/17/japan/society/record-high-inbound-travelers/.
UN Tourism (2023). International tourism and covid-19. https://www.unwto.org/tourism-data/global-and-regional-tourism-performance. Accessed on: May 1, 2023.
Valverde-Roda, J., Viruel, M., Casta˜no Prieto, L., and S´anchez, M. (2022). Interests, motivations and gastronomic experiences in the world heritage site destination of granada (spain): satisfaction analysis. British Food Journal, 125.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Wen, J., Huang, S. S., and Ying, T. (2019). Relationships between chinese cultural values and tourist motivations: A study of chinese tourists visiting israel. Journal of Destination Marketing & Management, 14:100367.
Xiang, Z., Schwartz, Z., Gerdes, J. H., and Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management, 44:120–130.
Yanuar, M. R. and Shiramatsu, S. (2020). Aspect extraction for tourist spot review in indonesian language using bert. In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pages 298–302. IEEE.
Ye, Q., Zhang, Z., and Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert systems with applications, 36(3):6527–6535.
Ying, X. (2019). An overview of overfitting and its solutions. In Journal of physics: Conference series, volume 1168, page 022022. IOP Publishing.
Zeng, B. (2021). Pattern of chinese tourist flows in japan: a social network analysis perspective. In Tourism Spaces, pages 42–64. Routledge.
Zeng, B. and He, Y. (2019). Factors influencing chinese tourist flow in japan–a grounded theory approach. Asia Pacific Journal of Tourism Research, 24(1):56–69.
Zhang, Y. and Wallace, B. (2016). A sensitivity analysis of (and practitioners’ guide to convolutional neural networks for sentence classification.
Downloads
Posted
Submitted: 2025-04-28 15:45:52 UTC
Published: 2025-05-07 06:27:33 UTC
License
Copyright (c) 2025
Zhenzhen Liu
Juuso Eronen
Fumito Masui
Michal Ptaszynski
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
