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Examining Airbnb guest satisfaction tendencies: a text mining approach

2022Journal article Restricted
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dc.contributor.authorCavique, Mariana
dc.contributor.authorRibeiro, Ricardo
dc.contributor.authorBatista, Fernando
dc.contributor.authorCorreia, Antónia
dc.date.accessioned2022-12-21T16:51:28Z
dc.date.available2022-12-21T16:51:28Z
dc.date.issued2022
dc.description.abstractGiven Airbnb's changes since its inception and the dynamism of customer preferences, a study that sheds light on how customer satisfaction is evolving is relevant. An automated method is proposed for identifying these satisfaction tendencies at a large scale. This study follows a text mining approach to analyse 590,070 reviews posted between 2010 and 2019 on the Airbnb platform in Lisbon. Topic Modelling is employed in order to identify the main topics discussed in the reviews, and Sentiment Analysis to understand the topics that compose guest's satisfaction in the context of Airbnb services. Three major topics are extracted from Airbnb reviews: 'host's service', 'physical aspects', and 'location'. Although a positivity bias in guest reviews is confirmed, the satisfaction level seems to be decreasing over the years. The results also reveal that 'physical aspects' is the predominant topic when considering the negative guest reviews. This research considers big data the base to create knowledge, data spanning over the years, offering consistency to the research.pt_PT
dc.description.sponsorshipUID/ECO/04007/2022
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1080/13683500.2022.2115877pt_PT
dc.identifier.issn1368-3500
dc.identifier.urihttp://hdl.handle.net/10400.1/18704
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherRoutledge Journalspt_PT
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.subjectAirbnbonline reviewspt_PT
dc.subjectTopic modellingpt_PT
dc.subjectSentiment analysispt_PT
dc.subjectSatisfactionpt_PT
dc.subjectHospitalitypt_PT
dc.titleExamining Airbnb guest satisfaction tendencies: a text mining approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
oaire.citation.endPage3622pt_PT
oaire.citation.issue22pt_PT
oaire.citation.startPage3607pt_PT
oaire.citation.titleCurrent Issues in Tourismpt_PT
oaire.citation.volume25pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameCorreia
person.givenNameAntónia
person.identifierR-000-K54
person.identifier.ciencia-id831F-FFD0-7A78
person.identifier.orcid0000-0002-6707-8289
person.identifier.scopus-author-id12140006700
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd3eb5da0-42d2-408b-af65-664563a18f5f
relation.isAuthorOfPublication.latestForDiscoveryd3eb5da0-42d2-408b-af65-664563a18f5f
relation.isProjectOfPublication0b14d63a-8f78-4e31-8a86-b72e1f07871f
relation.isProjectOfPublication.latestForDiscovery0b14d63a-8f78-4e31-8a86-b72e1f07871f

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