Clustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: the case of TripAdvisor

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dc.contributor.authorShu, Ziwei
dc.contributor.authorCarrasco González, Ramón Alberto
dc.contributor.authorPortela García-Miguel, Javier
dc.contributor.authorSanchez-Montañés, Manuel
dc.contributor.editorLin, Binshan
dc.date.accessioned2023-12-15T10:43:24Z
dc.date.available2023-12-15T10:43:24Z
dc.date.issued2023-03-01
dc.descriptionDataset related to this article can be found at https://doi.org/10.48550/arXiv.2002.06854, an open-access repository hosted atarXiv (Antognini & Faltings, 2020)
dc.description.abstractWith the growth of online tourism, it is important to analyze the reviews left by numerous customers on social networks to improve the hotel’s online reputation. Hotel segmentation based on online reviews has attracted an increasing interest from many academics. The problem is that many hotel segmentation models overlook the fact that some customers focus on positive reviews when choosing a hotel, while others focus on negative ones. To address this shortcoming, this paper develops a novel approach to classify hotels using the ordered weighted averaging (OWA) operator, the 2-tuple linguistic model, and K-means clustering. The proposed approach has been evaluated with a real dataset from TripAdvisor, which contains more than 50 million customer online reviews on eight aspects of the hotel. The results show that the proposed model can produce denser and more separated clusters than the model without linguistic quantifiers. From a business point of view, this model enables hotels to distinguish customers’ perceptions (from the less demanding to the most demanding) about their eight aspects, allowing them to specify which of them need to be improved and develop strategies more quickly. At the same time, it introduces a new way of ranking hotels online, allowing customers to create personalized rankings of hotels based on their degree of demand for various hotel aspects (better location, cleaner rooms, etc.) rather than the average ratings, so that they can select the most suitable hotels more quickly.
dc.description.departmentDepto. de Estadística y Ciencia de los Datos
dc.description.facultyFac. de Estudios Estadísticos
dc.description.refereedTRUE
dc.description.sponsorshipAgencia Estatal de Investigacion AEI/FEDER Spain
dc.description.sponsorshipComunidad Autonoma de Madrid, Spain
dc.description.statuspub
dc.identifier.doi10.1016/j.eswa.2022.118922
dc.identifier.issn0957-4174
dc.identifier.officialurlhttps://www.sciencedirect.com/science/article/pii/S0957417422019406?via%3Dihub
dc.identifier.urihttps://hdl.handle.net/20.500.14352/91317
dc.issue.numberPart A, 1
dc.journal.titleExpert Systems with Applications
dc.language.isoeng
dc.page.final22
dc.page.initial1
dc.publisherElsevier
dc.relation.projectIDPGC2018-095895- B-I00
dc.relation.projectIDPID2021-122347NB-I00
dc.relation.projectIDS2017/BMD-3688
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsrestricted access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.cdu519.22
dc.subject.cdu004.6
dc.subject.cdu338.48
dc.subject.keywordOrdered weighted averaging operators
dc.subject.keywordLinguistic quantifiers
dc.subject.keyword2-tuple linguistic model
dc.subject.keywordMulti-criteria decision-making
dc.subject.keywordSegmentation
dc.subject.keywordTripAdvisor
dc.subject.ucmEstadística
dc.subject.ucmTurismo
dc.subject.unesco1209.03 Análisis de Datos
dc.titleClustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: the case of TripAdvisor
dc.typejournal article
dc.type.hasVersionAM
dc.volume.number213
dspace.entity.typePublication
relation.isAuthorOfPublication658b3e73-df89-4013-b006-45ea9db05e25
relation.isAuthorOfPublication44f935e8-9acf-4613-ab4d-e007edda7540
relation.isAuthorOfPublication.latestForDiscovery658b3e73-df89-4013-b006-45ea9db05e25
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