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

Jing Wang () and Jianjun Zhu ()
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Jing Wang: Nanjing University of Aeronautics and Astronautics
Jianjun Zhu: Nanjing University of Aeronautics and Astronautics

A chapter in AI and Analytics for Smart Cities and Service Systems, 2021, pp 227-237 from Springer

Abstract: 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.

Keywords: Online hotel reviews; Customer preferences; Customer satisfaction; Text mining; Sentiment analysis (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-90275-9_19

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