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Machine learning for assessing quality of service in the hospitality sector based on customer reviews

Author

Listed:
  • Vladimir Vargas-Calderón

    (Laboratorios de Investigación en Inteligencia Artificial y Computación de Alto Desempeño, Human Brain Technologies
    Univesidad Nacional de Colombia)

  • Andreina Moros Ochoa

    (CESA, Colegio de Estudios Superiores de Administración)

  • Gilmer Yovani Castro Nieto

    (Pontificia Universidad Javeriana)

  • Jorge E. Camargo

    (Fundación Universitaria Konrad Lorenz)

Abstract

The increasing use of online hospitality platforms provides firsthand information about clients preferences, which are essential to improve hotel services and increase the quality of service perception. Customer reviews can be used to automatically extract the most relevant aspects of the quality of service for hospitality clientele. This paper proposes a framework for the assessment of the quality of service in the hospitality sector based on the exploitation of customer reviews through natural language processing and machine learning methods. The proposed framework automatically discovers the quality of service aspects relevant to hotel customers. Hotel reviews from Bogotá and Madrid are automatically scrapped from Booking.com. Semantic information is inferred through Latent Dirichlet Allocation and FastText, which allow representing text reviews as vectors. A dimensionality reduction technique is applied to visualise and interpret large amounts of customer reviews. Visualisations of the most important quality of service aspects are generated, allowing to qualitatively and quantitatively assess the quality of service. Results show that it is possible to automatically extract the main quality of service aspects perceived by customers from large customer review datasets. These findings could be used by hospitality managers to understand clients better and to improve the quality of service.

Suggested Citation

  • Vladimir Vargas-Calderón & Andreina Moros Ochoa & Gilmer Yovani Castro Nieto & Jorge E. Camargo, 2021. "Machine learning for assessing quality of service in the hospitality sector based on customer reviews," Information Technology & Tourism, Springer, vol. 23(3), pages 351-379, September.
    • Handle: RePEc:spr:infott:v:23:y:2021:i:3:d:10.1007_s40558-021-00207-4
    DOI: 10.1007/s40558-021-00207-4
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    References listed on IDEAS

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    1. Dhar, Rajib Lochan, 2015. "Service quality and the training of employees: The mediating role of organizational commitment," Tourism Management, Elsevier, vol. 46(C), pages 419-430.
    2. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    3. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    4. Septianto, Felix & Chiew, Tung Moi, 2018. "The effects of different, discrete positive emotions on electronic word-of-mouth," Journal of Retailing and Consumer Services, Elsevier, vol. 44(C), pages 1-10.
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