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Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA

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  • Ian Sutherland

    (Tourism Industry Data Analytics Lab (TIDAL), Department of Hospitality and Tourism Management, Sejong University, Seoul 05006, Korea)

  • Kiattipoom Kiatkawsin

    (Tourism Industry Data Analytics Lab (TIDAL), Department of Hospitality and Tourism Management, Sejong University, Seoul 05006, Korea)

Abstract

This study inductively analyzes the topics of interest that drive customer experience and satisfaction within the sharing economy of the accommodation sector. Using a dataset of 1,086,800 Airbnb reviews across New York City, the text is preprocessed and latent Dirichlet allocation is utilized in order to extract 43 topics of interest from the user-generated content. The topics fall into one of several categories, including the general evaluation of guests, centralized or decentralized location attributes of the accommodation, tangible and intangible characteristics of the listed units, management of the listing or unit, and service quality of the host. The deeper complex relationships between topics are explored in detail using hierarchical Ward Clustering.

Suggested Citation

  • Ian Sutherland & Kiattipoom Kiatkawsin, 2020. "Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3402-:d:348694
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    References listed on IDEAS

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    Cited by:

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    3. Zuo, Wenming & Bai, Weijing & Zhu, Wenfeng & He, Xinming & Qiu, Xinxin, 2022. "Changes in service quality of sharing accommodation: Evidence from airbnb," Technology in Society, Elsevier, vol. 71(C).
    4. Kiattipoom Kiatkawsin & Ian Sutherland & Jin-Young Kim, 2020. "A Comparative Automated Text Analysis of Airbnb Reviews in Hong Kong and Singapore Using Latent Dirichlet Allocation," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    5. Chrysa Agapitou & Anna Liana & Dimitrios Folinas & Aggeliki Konstantoglou, 2020. "Airbnb Is Customers’ Choice: Empirical Findings from a Survey," Sustainability, MDPI, vol. 12(15), pages 1-13, July.
    6. Adam Pawlicz & Ema Petaković & Ana-Marija Vrtodušić Hrgović, 2022. "Beyond Airbnb. Determinants of Customer Satisfaction in P2P Accommodation in Time of COVID-19," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    7. Zahyah H. Alharbi, 2023. "A Sustainable Price Prediction Model for Airbnb Listings Using Machine Learning and Sentiment Analysis," Sustainability, MDPI, vol. 15(17), pages 1-19, September.
    8. Villarroel Ordenes, Francisco & Silipo, Rosaria, 2021. "Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications," Journal of Business Research, Elsevier, vol. 137(C), pages 393-410.
    9. Yoon, Sang-Hyeak & Park, Ga-Yun & Kim, Hee-Woong, 2022. "Unraveling the relationship between the dimensions of user experience and user satisfaction: A smart speaker case," Technology in Society, Elsevier, vol. 71(C).
    10. I-Cheng Chang & Jeou-Shyan Horng & Chih-Hsing Liu & Sheng-Fang Chou & Tai-Yi Yu, 2022. "Exploration of Topic Classification in the Tourism Field with Text Mining Technology—A Case Study of the Academic Journal Papers," Sustainability, MDPI, vol. 14(7), pages 1-21, March.

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