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

dc.contributor.authorVargas Calderón, Vladimirspa
dc.contributor.authorMoros Ochoa, María Andreínaspa
dc.contributor.authorCastro Nieto, Gilmer Yovanispa
dc.contributor.authorCamargo, Jorge E.spa
dc.date.accessioned2023-06-21T22:23:00Z
dc.date.available2023-06-21T22:23:00Z
dc.date.issued2021-07-24
dc.identifier.issn1098-3058
dc.identifier.urihttp://hdl.handle.net/10726/5053
dc.language.isoeng
dc.publisherSpringer
dc.subjectQuality of service
dc.subjectNatural language processing
dc.subjectWord embedding
dc.subjectLatent topic analysis
dc.subjectDimensionality reduction
dc.titleMachine learning for assessing quality of service in the hospitality sector based on customer reviewseng
dc.typearticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.localAcceso Restringido
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.identifier.instnameinstname:Colegio de Estudios Superiores de Administración – CESA
dc.identifier.reponamereponame:Biblioteca Digital – CESA
dc.identifier.repourlrepourl:https://repository.cesa.edu.co/
dc.description.abstractenglishThe 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.eng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.relation.citationvolume23
dc.relation.citationstartpage351
dc.relation.citationendpage379
dc.contributor.orcidVargas Calderón, Vladimir [0000-0001-5476-3300]
dc.contributor.orcidMoros Ochoa, María Andreína [0000-0001-8428-9056]
dc.contributor.orcidCastro Nieto, Gilmer Yovani [0000-0001-9861-5588]
dc.type.driverinfo:eu-repo/semantics/article
dc.type.redcolhttp://purl.org/redcol/resource_type/ART
dc.type.coarversionhttp://purl.org/coar/version/c_71e4c1898caa6e32
dc.contributor.scopusVargas Calderón, Vladimir [57203879860]
dc.contributor.scopusMoros Ochoa, María Andreína [57195503017]
dc.contributor.scopusCastro Nieto, Gilmer Yovani [24544764500]
dc.contributor.scopusCamargo, Jorge E. [57192957971]
dc.description.orcidhttps://orcid.org/0000-0001-5476-3300
dc.description.orcidhttps://orcid.org/0000-0001-8428-9056
dc.description.orcidhttps://orcid.org/0000-0001-9861-5588
dc.description.scopushttps://www.scopus.com/authid/detail.uri?authorId=57203879860
dc.description.scopushttps://www.scopus.com/authid/detail.uri?authorId=57195503017
dc.description.scopushttps://www.scopus.com/authid/detail.uri?authorId=24544764500
dc.description.scopushttps://www.scopus.com/authid/detail.uri?authorId=57192957971
dc.identifier.eissn1943-4294
dc.relation.ispartofjournalInformation Technology & Tourism
dc.identifier.doihttps://doi.org/10.1007/s40558-021-00207-4
dc.rights.coarhttp://vocabularies.coar-repositories.org/access_rights/c_16ec/


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