Tourism profiling: a semi-automatic classification model of points of interest

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dc.creatorAmarildo Martins de Magalhães
dc.creatorRenata Maria Abrantes Baracho Porto
dc.creatorThomas Mandl
dc.date.accessioned2025-07-22T16:20:35Z
dc.date.accessioned2025-09-08T23:52:43Z
dc.date.available2025-07-22T16:20:35Z
dc.date.issued2021
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.format.mimetypepdf
dc.identifier.issn1690-4524
dc.identifier.urihttps://hdl.handle.net/1843/83732
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofJournal of Systemics, Cybernetics and Informatics
dc.rightsAcesso Aberto
dc.subjectOrganização da informação
dc.subjectAprendizado de máquina
dc.subjectTurista - Comportamento
dc.subject.otherinformation retrieval
dc.subject.otherinformation system
dc.subject.otherMachine-Learning
dc.subject.otherKnowledge Organization System
dc.subject.otherModelagem de Informação
dc.titleTourism profiling: a semi-automatic classification model of points of interest
dc.typeArtigo de periódico
local.citation.epage56
local.citation.issue5
local.citation.spage49
local.citation.volume18
local.description.resumoReviews are a powerful source of information that helps tourists in their decision-making process. However, using this volume of data to make decisions it is time consuming. For example, the city Foz do Iguaçu, located in Brazil, has more than 44k reviews on TripAdvisor. Based on these opinions, how could a tourist understand if this attraction is good for families, a romantic date, or if it offers a good outdoor experience? Moreover, which other attractions could offer similar experiences? These questions motivated this research, as we try to address the problem of classifying tourism attractions/destinations in profiles. We proposed a hybrid approach, using experts’ knowledge and machine-learning with semi-automatic classification models to solve the problem. This paper presents a new approach to classify tourism attractions in profiles using reviews. Our findingsshow that, the most visited places are not necessarily the most relevant to a specific profile and as such the corresponding group of tourists. Understanding these profiles can aid the discovery or the selection of a travel destination. In addition, it allows governments and the private sector to target tourism marketing actions in the most assertive way.
local.publisher.countryBrasil
local.publisher.departmentARQ - DEPARTAMENTO DE TEC ARQUITETURA E URBANISMO
local.publisher.initialsUFMG
local.url.externahttp://www.iiisci.org/journal/sci/FullText.asp?var=&id=SA019UC20

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