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Research on Hotel Customer Preferences and Satisfaction Based on Text Mining: Taking Ctrip Hotel Reviews as an Example

In: AI and Analytics for Smart Cities and Service Systems

Author

Listed:
  • Jing Wang

    (Nanjing University of Aeronautics and Astronautics)

  • Jianjun Zhu

    (Nanjing University of Aeronautics and Astronautics)

Abstract

In the hotel industry, online reservation has become one of the main ways to gain customers, which has brought huge profits. Online hotel review analysis can help hoteliers get customer feedback and improve service quality, so as to enhance their competitiveness. Hence, this study proposes a complete online hotel review analysis process based on text mining to reflect customer preferences and satisfaction, improving the accuracy of consumer preference factor extraction, using sentiment score to indicate customer satisfaction to supplement comprehensive evaluation method mentioned commonly in the existing processes. The value of the proposed process was demonstrated through an example using online Ctrip hotel reviews. It showed that customers prefer three factors: environment, service and location. The study also revealed customer satisfaction by sentiment score distribution graphs. The conclusions and future suggestions are described at the end.

Suggested Citation

  • Jing Wang & Jianjun Zhu, 2021. "Research on Hotel Customer Preferences and Satisfaction Based on Text Mining: Taking Ctrip Hotel Reviews as an Example," Lecture Notes in Operations Research, in: Robin Qiu & Kelly Lyons & Weiwei Chen (ed.), AI and Analytics for Smart Cities and Service Systems, pages 227-237, Springer.
    • Handle: RePEc:spr:lnopch:978-3-030-90275-9_19
    DOI: 10.1007/978-3-030-90275-9_19
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