  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.
