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Word-of-Mouth Evaluation of Ancient Towns in Southern China Using Web Comments

Author

Listed:
  • Yihan Zhang

    (School of Geography and Environmental Economics, Guangdong University of Finance and Economics, Guangzhou 510320, China)

  • Weizhuo Guo

    (School of Geography and Environmental Economics, Guangdong University of Finance and Economics, Guangzhou 510320, China)

  • Yanling Sheng

    (School of Geography and Environmental Economics, Guangdong University of Finance and Economics, Guangzhou 510320, China)

  • Shanshan Li

    (School of Geography and Environmental Economics, Guangdong University of Finance and Economics, Guangzhou 510320, China)

Abstract

With the rapid development of digital networks and communication technologies, traditional word-of-mouth (WOM) has transformed into electronic word-of-mouth (eWOM), which plays a pivotal role in improving the management and service quality of ancient town tourism. This study uses Python web scraping techniques to gather eWOM data from the top ten ancient towns in southern China. Using IPA analysis, the analytic hierarchy process (AHP), Term Frequency–Inverse Document Frequency (TF-IDF), and cluster analysis, we developed a comprehensive eWOM evaluation framework. This framework was employed to perform word frequency analysis, sentiment analysis, topic modeling, and rating analysis, providing deeper insights into tourists’ perceptions. The results reveal several key findings: (1) Transportation infrastructure varies significantly across the towns. Heshun and Huangyao suffer from poor accessibility, while the remaining towns benefit from the developed transportation network of the Yangtze River Delta. (2) The volume of eWOM is strongly influenced by seasonal patterns and was notably impacted by the COVID-19 pandemic. (3) The majority of tourists express positive sentiments toward the ancient towns, with a focus on the available facilities. Their highest levels of satisfaction, however, are associated with the scenic landscapes. (4) A comprehensive eWOM analysis suggests that Wuzhen and Xidi–Hongcun are the most popular tourist destinations, while Zhujiajiao, Huangyao, Zhouzhuang, and Nanxun exhibit lower levels of both attention and visitor satisfaction.

Suggested Citation

  • Yihan Zhang & Weizhuo Guo & Yanling Sheng & Shanshan Li, 2025. "Word-of-Mouth Evaluation of Ancient Towns in Southern China Using Web Comments," Tourism and Hospitality, MDPI, vol. 6(1), pages 1-22, February.
    • Handle: RePEc:gam:jtourh:v:6:y:2025:i:1:p:25-:d:1588175
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    References listed on IDEAS

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    1. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
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    4. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. Yu, Jiefei & Hu, Yanqing & Yu, Min & Di, Zengru, 2010. "Analyzing netizens’ view and reply behaviors on the forum," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3267-3273.
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