Know your Hotels Well! an Online Review Analysis using Text Analytics

  • Authors

    • Angela S.H Lee
    • Zaharin Yusoff
    • Zuraini Zainol
    • Pillai V
    https://doi.org/10.14419/ijet.v7i4.31.23406

    Received date: December 8, 2018

    Accepted date: December 8, 2018

    Published date: December 9, 2018

  • Opinion Mining, Text mining, Hotel Reviews, Customer Satisfaction, SAS TexMiner

  • Abstract

    Online travel forums have become an extremely popular platform for sharing travel information, with a large number of reviews being posted daily. Travel websites such as TripAdvisor and Booking.com have turned into very important resources for hotel operators and travellers alike, for promoting hotel rooms, choosing hotels as well as for soliciting and sharing feedback. Criticisms, compliments, dissensions, etc., are now accessible anytime and anywhere on the web, and can be readily amassed, while opinion mining techniques have developed rapidly. Together they provide the opportunity and capability to analyse and deduce factors that influence travellers in their choice of hotels. In this paper, we apply opinion mining on data collected from Tripadvisor websites. In total, 11,130 reviews on 4 hotels within the four-star and five-star categories in Kuala Lumpur are crawled, collected, and mined to identify the top-k most predominant information based on the most frequent and most related terms used in describing each of the chosen hotels. The results of this study would allow travellers to see the opinions of other travellers on these hotels, and hotel operators would be able to receive feedback to improve their services and in turn promote their hotels. This study is also carried out in view of future improvements in the techniques used and the analysis performed.

  • References

    1. Werbler, C. Online Consumer Reviews Significantly Impact Con-sumer Purchasing Decisions, New Opinion Research Corporation Survey Finds, (2008). [online] Businesswire.com. Available at: https://www.businesswire.com/news/home/20080624005206/en/Online-Consumer-Reviews-Significantly-Impact-Consumer-Purchasing.
    2. Verma, R., Victorino, L., Karniouchina, K and Feickert, J. Seg-menting Hotel Customers based on the technology readiness index. Cornell Hospitability Report, (2007), pp, 4-13.
    3. Berezina, K., Cobanoglu, C., Miller, B. L., & Kwansa, F. A. The impact of information security breach on hotel guest perception of service quality, satisfaction, revisit intentions and word-of-mouth. International Journal of Contemporary Hospitality Management, (2012), 24(7), 991–1010.
    4. L. McGarrity, "What Sentiment Analysis Can Do for Your Brand," Marketing Profs, (2016). [Online]. Available: http://www.marketingprofs.com/opinions/2016/29673/what-sentiment-analysis-can-do-for-your-brand. [Accessed 11 April 2017].
    5. M. Darini and F. Khozaei, "The study of factors affecting customer’s satisfaction with three star hotels in Dubai," International Journal of Advanced Engineering, Management and Science,(2016),2 (2), pp. 21-24.
    6. Z. Xiang, Z. Schwartz, J. H. G. Jr and M. Uysal, "What can big data and text analytics tell us about hotel guestexperience and satisfaction?," International Journal of Hospitality Management, (2015), vol. 44, pp. 120-130.
    7. R. Byng, "15 Years of TripAdvisor: How 200 Million Reviews Changed the Way We Travel," Independent UK, 25 February 2015. [Online]. Available: http://www.independent.co.uk/travel/15-years-of-tripadvisor-how-200-million-reviews-changed-the-way-we-travel-10070175.html. [Accessed 14 April 2017].
    8. Nicholas, C. K. W., & Lee, A. S. H. (2017). Voice of customers: Text analysis of hotel customer reviews (cleanliness, overall envi-ronment & value for money). In Proceedings of the 2017 Interna-tional Conference on Big Data Research, (2017), pp. 104-111. ACM.
    9. Lau, K., Lee, K. and Ho, Y. Text Mining for the Hotel Industry. Cornell Hotel and Restaurant Administration Quarterly, (2005), 46(3), pp.344-362.
    10. Gémar, G. and Jiménez-Quintero, J. (2015). Text mining social me-dia for competitive analysis. Tourism & Management Stud-ies,(2015),11(1),pp.84-90. [Online] Available at: http://www.academia.edu/16536522/Text_mining_social_media_for_competitive_analysis.
    11. Cantallops, A. S., & Salvi, F. New consumer behavior: A review of research on eWOM and hotels. International Journal of Hospitality Management, (2014), 36, 41–51.
    12. Litvin, S. W. , Goldsmith, R. E. , & Pan, B. Electronic word-of-mouth in hospitality and tourism management. Tourism manage-ment, (2008), 29 (3), 458–468
    13. Pizam, A., & Ellis, T. Customer satisfaction and its measurement in hospital-ity enterprises. International Journal of Contemporary Hos-pitality Management, (1999),11(7), 326–339.
    14. Ekiz, E., & Au, N. Comparing Chinese and American attitudes to-wardscomplaining. International Journal of Contemporary Hospital-ity Management,(2011), 23(3), 327–343
    15. Cranage, D. A., & Mattila, A. S. Service recovery and pre-emptive strate-gies for service failure: Both lead to customer satisfaction and loyalty, but fordifferent reasons. Journal of Hospitality & Leisure Marketing, (2005), 13(3/4), 161–181
    16. Mattila, A. S., & Mount, D. J. The impact of selected customer characteristicsand response time on e-complaint satisfaction and re-turn intent. International Journal of Hospitality Management, (2003), 22(2), 135–145.
    17. Kucuk, U. S., & Krishnamurthy, S. An analysis of consumer power on the Internet. Technovation,(2007), 27(1/2), 47–56
    18. Barreda, A., & Bilgihan, A. An analysis of user generated content for hotelexperiences. Journal of Hospitality and Tourism Technolo-gy, (2013), 4(3), 263–280.
    19. García-Barriocanal, E., Sicilia, M.-A., & Korfiatis, N. Exploring ho-tel ser-vice quality experience indicators in user-generated content: A case usingTripadvisor data. In Mediterranean Conference on In-formation Systems, (2010) Proceedings, 33.
    20. O’Connor, P. Managing a hotel’s image on TripAdvisor. Journal of Hospitality Marketing & Management, (2010), 19(7), 754–772
    21. A. L. S. Hoong, T. M. Lim, S. K. Leow and J. L. R. Aun, "A study on the use of “Yams” for enterprise knowledge sharing," Digital In-formation and Communication Technology and it's Applications (DICTAP), 2012 Second International Conference on, Bangkok, 2012, pp. 183-188.doi: 10.1109/DICTAP.2012.6215348
    22. Lee A.S.H, K.L Chong & Nicholas, C. K. W. (2018). “OpinionSeer: Text Visualization on Hotel Customer Reviews of Services and Physical Environment”. 9th ICATSE Conference on Information Science and Applications. (In Press)
    23. Zainol Z, Nohuddin PNE, Noormanshah WMU & Hanafi AHM, M. “Visualization of context-based keyword pattern cluster analysis on tacit knowledge among officer cadets at Universiti Pertahanan Nasional Malaysia (UPNM)”. Advanced Science Letters, Vol.24, No.3, (2018), pp.1550-1554, available online:https://www.ingentaconnect.com/contentone/asp/asl/2018/00000024/00000003/art00003, last visit:10.06.2018
    24. Zainol Z, Nohuddin PNE, Mohd TAT & Zakaria O, “Text analytics of unstructured textual data: a study on military peacekeeping doc-ument using R text mining package”. Proceeding of the 6th Interna-tional Conference on Computing and Informatics (ICOCI17), (2017), pp. 1–7.

  • Downloads

  • How to Cite

    S.H Lee, A., Yusoff, Z., Zainol, Z., & V, P. (2018). Know your Hotels Well! an Online Review Analysis using Text Analytics. International Journal of Engineering and Technology, 7(4.31), 341-347. https://doi.org/10.14419/ijet.v7i4.31.23406