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

• URL: http://arxiv.org/abs/2107.10328v1
• Date: Wed, 21 Jul 2021 19:45:40 GMT
• Title: Machine learning for assessing quality of service in the hospitality sector based on customer reviews
• Authors: Vladimir Vargas-Calder\'on, Andreina Moros Ochoa, Gilmer Yovani Castro Nieto and Jorge E. Camargo
• Abstract summary: This paper proposes a framework for the assessment of the quality of service in the hospitality sector based on the exploitation of customer reviews. The proposed framework automatically discovers the quality of service aspects relevant to hotel customers. Visualisations of the most important quality of service aspects are generated, allowing to qualitatively and quantitatively assess the quality of service.
• Score: 0.16058099298620418
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 Bogot\'a 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.

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