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Understanding customers' hotel revisiting behaviour: a sentiment analysis of online feedback reviews

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  • Eunil Park
  • Jiwon Kang
  • Daejin Choi
  • Jinyoung Han

Abstract

Customer retention has been one of the most recognized research issues in the service industry. The next on the list is predicting customer behaviour or understanding customer intent, which is particularly important for the hospitality and tourism industries. This study investigates the customers' hotel revisiting behaviour using a large-scale customer review data, which can shed light on the potentiality of (i) identifying the factors that are associated with the customer revisiting behaviour and attracting more customers to reuse their services and (ii) predicting future customer revisiting behaviour to a hotel. We analyse the data of 105,126 customers of an online hotel reservation service, and conduct a sentimental analysis on the user feedback reviews. By comparing one-time visitors and re-visitors, our analysis shows that the feedback reviews of re-visitors tend to (i) contain more words in a sentence and (ii) reveal more positive/negative sentiments than those of one-time visitors. On the other hand, the feedback reviews of one-time visitors tended to include more analytical and anxious words than those of re-visitors. The findings can serve as a foundation for the use of big data analysis in hospitality and tourism research.

Suggested Citation

  • Eunil Park & Jiwon Kang & Daejin Choi & Jinyoung Han, 2020. "Understanding customers' hotel revisiting behaviour: a sentiment analysis of online feedback reviews," Current Issues in Tourism, Taylor & Francis Journals, vol. 23(5), pages 605-611, March.
    • Handle: RePEc:taf:rcitxx:v:23:y:2020:i:5:p:605-611
    DOI: 10.1080/13683500.2018.1549025
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    Cited by:

    1. Camelia Surugiu & Marius-Răzvan Surugiu & Cătălin Grădinaru, 2023. "Targeting Creativity Through Sentiment Analysis: A Survey on Bucharest City Tourism," SAGE Open, , vol. 13(2), pages 21582440231, April.
    2. Alireza Alaei & Ying Wang & Vinh Bui & Bela Stantic, 2023. "Target-Oriented Data Annotation for Emotion and Sentiment Analysis in Tourism Related Social Media Data," Future Internet, MDPI, vol. 15(4), pages 1-21, April.
    3. Edmond H. C. Wu & Jihao Hu & Rui Chen, 2022. "Monitoring and forecasting COVID-19 impacts on hotel occupancy rates with daily visitor arrivals and search queries," Current Issues in Tourism, Taylor & Francis Journals, vol. 25(3), pages 490-507, February.
    4. Shah, Adnan Muhammad & Abbasi, Amir Zaib & Yan, Xiangbin, 2023. "Do online peer reviews stimulate diners’ continued log-in behavior: Investigating the role of emotions in the O2O meal delivery apps context," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    5. Archana Yadav & Biswajit Patra & Tanmay Basu, 2024. "Modeling International Tourist Arrivals: An NLP Perspective," SN Operations Research Forum, Springer, vol. 5(4), pages 1-19, December.
    6. Víctor Calderón-Fajardo & Miguel Puig-Cabrera & Ignacio Rodríguez-Rodríguez, 2024. "Deciphering Tourism’s Role in Antarctica’s Geosocial Concerns through Data Mining Techniques," Land, MDPI, vol. 13(6), pages 1-22, June.

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