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Understanding Online Hotel Reviews Through Automated Text Analysis

Author

Listed:
  • Shawn Mankad

    (EM - EMLyon Business School)

  • Hyunjeong Spring Han
  • Joel Goh
  • Srinagesh Gavirneni

Abstract

Customer reviews submitted at Internet travel portals are an important yet underexplored new resource for obtaining feedback on customer experience for the hospitality industry. These data are often voluminous and unstructured, presenting analytical challenges for traditional tools that were designed for well-structured, quantitative data. We adapt methods from natural language processing and machine learning to illustrate how the hotel industry can leverage this new data source by performing automated evaluation of the quality of writing, sentiment estimation, and topic extraction. By analyzing 5,830 reviews from 57 hotels in Moscow, Russia, we find that (i) negative reviews tend to focus on a small number of topics, whereas positive reviews tend to touch on a greater number of topics; (ii) negative sentiment inherent in a review has a larger downward impact than corresponding positive sentiment; and (iii) negative reviews contain a larger variation in sentiment on average than positive reviews. These insights can be instrumental in helping hotels achieve their strategic, financial, and operational objectives.

Suggested Citation

  • Shawn Mankad & Hyunjeong Spring Han & Joel Goh & Srinagesh Gavirneni, 2016. "Understanding Online Hotel Reviews Through Automated Text Analysis," Post-Print hal-02311939, HAL.
    • Handle: RePEc:hal:journl:hal-02311939
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    Citations

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    Cited by:

    1. Manosso, Franciele Cristina & Domareski Ruiz, Thays Cristina, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7, pages 16-27.
    2. Grybauskas, Andrius & Stefanini, Alessandro & Ghobakhloo, Morteza, 2022. "Social sustainability in the age of digitalization: A systematic literature Review on the social implications of industry 4.0," Technology in Society, Elsevier, vol. 70(C).
    3. Chiehyeon Lim & Paul P. Maglio, 2018. "Data-Driven Understanding of Smart Service Systems Through Text Mining," Service Science, INFORMS, vol. 10(2), pages 154-180, June.
    4. Woohyuk Kim & Sung-Bum Kim & Eunhye Park, 2021. "Mapping Tourists’ Destination (Dis)Satisfaction Attributes with User-Generated Content," Sustainability, MDPI, vol. 13(22), pages 1-16, November.
    5. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    6. Seungju Nam & Chunghun Ha & Hyun Cheol Lee, 2018. "Redesigning In-Flight Service with Service Blueprint Based on Text Analysis," Sustainability, MDPI, vol. 10(12), pages 1-21, November.
    7. Lee, Changhun & Lim, Chiehyeon, 2021. "From technological development to social advance: A review of Industry 4.0 through machine learning," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    8. María D. Illescas-Manzano & Sergio Martínez-Puertas & Gema M. Marín-Carrillo & María B. Marín-Carrillo, 2023. "Dynamics of agglomeration and competition in the hotel industry: A geographically weighted regression analysis based on an analytical hierarchy process and geographic information systems (GIS) data," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 213-252, March.
    9. Kim, Kun & Park, Oun-joung & Yun, Seunghyun & Yun, Haejung, 2017. "What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 362-369.
    10. Matthew J. Schneider & Shawn Mankad, 2021. "A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(3), pages 66-83, September.
    11. Junegak Joung & Kiwook Jung & Sanghyun Ko & Kwangsoo Kim, 2018. "Customer Complaints Analysis Using Text Mining and Outcome-Driven Innovation Method for Market-Oriented Product Development," Sustainability, MDPI, vol. 11(1), pages 1-14, December.
    12. Zajadacz Alina & Minkwitz Aleksandra, 2020. "Using Social Media Data to Plan for Tourism," Quaestiones Geographicae, Sciendo, vol. 39(3), pages 125-138, September.
    13. Himanshu Sharma & Abhishek Tandon & P. K. Kapur & Anu G. Aggarwal, 2019. "Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 973-983, October.
    14. Yang Qian & Yuanchun Jiang & Yanan Du & Jianshan Sun & Yezheng Liu, 2020. "Segmenting market structure from multi-channel clickstream data: a novel generative model," Electronic Commerce Research, Springer, vol. 20(3), pages 509-533, September.
    15. Seungju Nam & Hyun Cheol Lee, 2019. "A Text Analytics-Based Importance Performance Analysis and Its Application to Airline Service," Sustainability, MDPI, vol. 11(21), pages 1-24, November.
    16. Lim, Chiehyeon & Cho, Gi-Hyoug & Kim, Jeongseob, 2021. "Understanding the linkages of smart-city technologies and applications: Key lessons from a text mining approach and a call for future research," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    17. Vera L. Miguéis & Henriqueta Nóvoa, 2017. "Exploring Online Travel Reviews Using Data Analytics: An Exploratory Study," Service Science, INFORMS, vol. 9(4), pages 315-323, December.
    18. Cristina Franciele & Thays Christina Domareski Ruiz, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," Post-Print hal-03373984, HAL.
    19. Kolomoyets, Yuliya & Dickinger, Astrid, 2023. "Understanding value perceptions and propositions: A machine learning approach," Journal of Business Research, Elsevier, vol. 154(C).
    20. Xiaobin Zhang & Hak-Seon Kim, 2021. "Customer Experience and Satisfaction of Disneyland Hotel through Big Data Analysis of Online Customer Reviews," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    21. Jun Hwan Kim & Hyun Cheol Lee, 2019. "Understanding the Repurchase Intention of Premium Economy Passengers Using an Extended Theory of Planned Behavior," Sustainability, MDPI, vol. 11(11), pages 1-19, June.
    22. Lifeng He & Dongmei Han & Xiaohang Zhou & Zheng Qu, 2020. "The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model," IJERPH, MDPI, vol. 17(10), pages 1-18, May.

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