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The increasing use of online hospitality platforms provides firsthandinformation about clients preferences, which are essential to improve hotelservices and increase the quality of service perception. Customer reviews canbe used to automatically extract the most relevant aspects of the quality ofservice for hospitality clientele. This paper proposes a framework for theassessment of the quality of service in the hospitality sector based on theexploitation of customer reviews through natural language processing andmachine learning methods. The proposed framework automatically discovers thequality of service aspects relevant to hotel customers. Hotel reviews fromBogot\'a and Madrid are automatically scrapped from Booking.com. Semanticinformation is inferred through Latent Dirichlet Allocation and FastText, whichallow representing text reviews as vectors. A dimensionality reductiontechnique is applied to visualise and interpret large amounts of customerreviews. Visualisations of the most important quality of service aspects aregenerated, allowing to qualitatively and quantitatively assess the quality ofservice. Results show that it is possible to automatically extract the mainquality of service aspects perceived by customers from large customer reviewdatasets. These findings could be used by hospitality managers to understandclients better and to improve the quality of service.

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