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Exploring destination image through online reviews: an augmented mining model using latent Dirichlet allocation combined with probabilistic hesitant fuzzy algorithm

Yuyan Luo (College of Management Science, Chengdu University of Technology, Chengdu, China)
Tao Tong (College of Management Science, Chengdu University of Technology, Chengdu, China)
Xiaoxu Zhang (College of Management Science, Chengdu University of Technology, Chengdu, China)
Zheng Yang (College of Management Science, Chengdu University of Technology, Chengdu, China)
Ling Li (College of Management Science, Chengdu University of Technology, Chengdu, China)

Kybernetes

ISSN: 0368-492X

Article publication date: 9 November 2021

Issue publication date: 3 March 2023

448

Abstract

Purpose

In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.

Design/methodology/approach

The study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.

Findings

In the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.

Originality/value

Previous research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

Keywords

Acknowledgements

The 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), Regional Public Management Informatization Research Center Project of Sichuan Province, Key Research Bases of Philosophy and Social Sciences (Grant No. QGXH20-03), 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), Key Program of Leisure and Sports Development Research Center in Sichuan Province, Key Research Base of Humanity and Social Science, Education Department of Sichuan Province (Grant No. XXTYCY2021A01), Program of Ecological Civilization Research Center in West China, and Key College Research Base of Humanity and Social Science in Sichuan Province (Grant No. XBST2021-YB001).

Citation

Luo, Y., Tong, T., Zhang, X., Yang, Z. and Li, L. (2023), "Exploring destination image through online reviews: an augmented mining model using latent Dirichlet allocation combined with probabilistic hesitant fuzzy algorithm", Kybernetes, Vol. 52 No. 3, pp. 874-897. https://doi.org/10.1108/K-07-2021-0584

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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