#6153. Machine learning for assessing quality of service in the hospitality sector based on customer reviews

September 2026publication date
Proposal available till 10-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Tourism, Leisure and Hospitality Management;
Computer Science Applications;
Information Systems;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
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? 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.
Keywords:
Dimensionality reduction; Latent topic analysis; Natural language processing; Quality of service; Word embedding

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