Abstract
With the acceleration of Internet informatization and the vigorous development of social media, tourists are inclined to share their travel feelings online. This paper proposes a new framework for exploring the image of destinations from the perspective of tourists based on the online reviews of scenic spots. This study divides the image of destinations into three dimensions, mining the information of online reviews through statistical analysis and text matching, and analyzing the LDA topic model and emotional tendency. Combining the theory and method of probabilistic linguistic term set to obtain the element endowment information of scenic spots and form the destination image. Finally, the proposed method is successfully applied to the image perception of 10 5A scenic spots, and relevant policy suggestions are provided for managers.
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Funding
This research was funded by the Humanities and Social Sciences Program of the Ministry of Education of the People’s Republic of China (Grant No. 20YJC630095), National Natural Science Foundation of China (Grant No. 71971151), China’s Post-doctoral Science Fund Project (Grant No. 2018M631069), Key Project of Sichuan Leisure Sports Industry Development and Research Center (Grant No. XXTYCY2021A01), National Park Research Center Project of Sichuan Province Social Science Key Research Base (Extension) (Grant No. GJGY2020-ZD001), Sichuan Provincial Social Science Research Planning Project (Grant No. SC21B007).
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All authors contributed to the study conception and design. Conceptualization, methodology, and data analysis were performed by YL, JH and ZY. Conclusion and discussion were performed by JW and RL. The first draft of the manuscript was prepared and written by YL, JH and ZY, and all authors commented on previous versions of the manuscript. The revised draft was made by YL and RL, and all authors read and approved the final manuscript.
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Luo, Y., He, J., Yang, Z. et al. Exploring the destination image based on the perspective of tourists’ expression using machine learning methods combined with PLTS-PT. Soft Comput 27, 5537–5552 (2023). https://doi.org/10.1007/s00500-023-07815-8
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DOI: https://doi.org/10.1007/s00500-023-07815-8