Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand

Detalhes bibliográficos
Autor(a) principal: Borges , Júnia Lúcio de Castro
Data de Publicação: 2024
Outros Autores: Riani Costa Perinotto , André, de Souza Braga, Solano
Tipo de documento: Artigo
Idioma: por
eng
Título da fonte: Marketing & Tourism Review
Texto Completo: https://revistas.face.ufmg.br/index.php/mtr/article/view/8168
Resumo: This study applied text data mining techniques, statistical text analysis and network analysis in a qualitative database with information resulting from comments from social media specialized in tourism in order to identify the perception of visitors to the National Parks of Piauí. These conservation units are important tourist assets because they house a great natural heritage with extreme historical relevance with thousands of archaeological sites. The methodology developed in this article presents defensible and reproducible criteria to be replicated in any tourist attraction present on TripAdvisor, and greatly expands the understanding about the perception of visitors and the essence of places, facilitating the decision making of planners and managers of tourist destinations. The presented results were achieved from the use of automation techniques and computational programming to extract a large volume of data - Big Data - from digital footprints left by travelers on TripAdvisor, regarding the tourist attractions of interest. The qualitative database was mined using free software aimed at textual analysis, statistical treatment and network analysis. Based on the results, it was possible to identify key aspects regarding the tourist destinations such as the centrality of themes regarding archaeological aspects and the monitoring of local guides.
id UFMG-16_78584e22810b86ba10ff7dc62d3f0262
oai_identifier_str oai:ojs.pkp.sfu.ca:article/8168
network_acronym_str UFMG-16
network_name_str Marketing & Tourism Review
repository_id_str
spelling Crowdsourcing, Big Data and Network analysis techniques applied to Tourist DemandO uso de técnicas de Crowdsourcing, Big Data e análise de Redes aplicadas à Demanda Turística TripAdvisorBig DataTourist Demanddigital footprintTourism PlanningTripAdvisorBig DataDemanda Turísticapegadas digitaisPlanejamento TurísticoThis study applied text data mining techniques, statistical text analysis and network analysis in a qualitative database with information resulting from comments from social media specialized in tourism in order to identify the perception of visitors to the National Parks of Piauí. These conservation units are important tourist assets because they house a great natural heritage with extreme historical relevance with thousands of archaeological sites. The methodology developed in this article presents defensible and reproducible criteria to be replicated in any tourist attraction present on TripAdvisor, and greatly expands the understanding about the perception of visitors and the essence of places, facilitating the decision making of planners and managers of tourist destinations. The presented results were achieved from the use of automation techniques and computational programming to extract a large volume of data - Big Data - from digital footprints left by travelers on TripAdvisor, regarding the tourist attractions of interest. The qualitative database was mined using free software aimed at textual analysis, statistical treatment and network analysis. Based on the results, it was possible to identify key aspects regarding the tourist destinations such as the centrality of themes regarding archaeological aspects and the monitoring of local guides.O presente estudo aplicou técnicas de mineração de dados textuais, análise textual estatística e análise de redes em um banco de dados qualitativo com informações resultantes de comentários provenientes de mídia social especializada em turismo com vistas a analisar o discurso dos visitantes dos Parques Nacionais do Piauí. Estas unidades de conservação são importantes ativos turísticos porque além de abrigarem grande patrimônio natural, são de extrema relevância histórica por conterem milhares de sítios arqueológicos. A metodologia desenvolvida neste artigo apresenta critérios defensáveis e reproduzíveis para ser replicada em qualquer atrativo turístico presente no TripAdvisor, e amplia sobremaneira a compreensão acerca da percepção dos visitantes e da essência dos lugares, facilitando a tomada de decisões de planejadores e gestores de destinos turísticos. Os resultados apresentados foram alcançados a partir do uso de técnicas de automação e programação computacional para extrair um grande volume de dados - Big Data - de rastros (pegadas digitais) deixados pelos viajantes no TripAdvisor, a respeito dos atrativos turísticos de interesse. O banco de dados qualitativos foi minerado com o uso de softwares livres voltados à análise textual, tratamento dos dados e análise das redes. A partir dos resultados foi possível identificar aspectos fundamentais a respeito dos destinos turísticos ora destacados como, por exemplo, a centralidade das temáticas a respeito dos aspectos arqueológicos e o acompanhamento de guias. O presente estudo aplicou técnicas de mineração de dados textuais, análise textual estatística e análise de redes em um banco de dados qualitativo com informações resultantes de comentários provenientes de mídia social especializada em turismo com vistas a analisar o discurso dos visitantes dos Parques Nacionais do Piauí. Estas unidades de conservação são importantes ativos turísticos porque além de abrigarem grande patrimônio natural, são de extrema relevância histórica por conterem milhares de sítios arqueológicos. A metodologia desenvolvida neste artigo apresenta critérios defensáveis e reproduzíveis para ser replicada em qualquer atrativo turístico presente no TripAdvisor, e amplia sobremaneira a compreensão acerca da percepção dos visitantes e da essência dos lugares, facilitando a tomada de decisões de planejadores e gestores de destinos turísticos. Os resultados apresentados foram alcançados a partir do uso de técnicas de automação e programação computacional para extrair um grande volume de dados - Big Data - de rastros (pegadas digitais) deixados pelos viajantes no TripAdvisor, a respeito dos atrativos turísticos de interesse. O banco de dados qualitativos foi minerado com o uso de softwares livres voltados à análise textual, tratamento dos dados e análise das redes. A partir dos resultados foi possível identificar aspectos fundamentais a respeito dos destinos turísticos ora destacados como, por exemplo, a centralidade das temáticas a respeito dos aspectos arqueológicos e o acompanhamento de guias.  Federal University of Minas Gerais2024-03-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://revistas.face.ufmg.br/index.php/mtr/article/view/816810.29149/mtr.v9i1.8168Marketing & Tourism Review; Vol. 9 No. 1 (2024): v.9, n.1, 2024Marketing & Tourism Review; v. 9 n. 1 (2024): v.9, n.1, 20242525-81762525-817610.29149/mtr.v9i1reponame:Marketing & Tourism Reviewinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGporenghttps://revistas.face.ufmg.br/index.php/mtr/article/view/8168/4091https://revistas.face.ufmg.br/index.php/mtr/article/view/8168/4092Copyright (c) 2024 Júnia Lúcio de Castro Borges , André Riani Costa Perinotto , Solano de Souza Bragahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess Borges , Júnia Lúcio de CastroRiani Costa Perinotto , Andréde Souza Braga, Solano 2024-05-05T00:56:44Zoai:ojs.pkp.sfu.ca:article/8168Revistahttps://revistas.face.ufmg.br/index.php/mtrPUBhttps://revistas.face.ufmg.br/index.php/mtr/oaimkt.tourism.review@gmail.com||mg.ufmg@gmail.com2525-81762525-8176opendoar:2024-05-05T00:56:44Marketing & Tourism Review - Universidade Federal de Minas Gerais (UFMG)false
dc.title.none.fl_str_mv Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand
O uso de técnicas de Crowdsourcing, Big Data e análise de Redes aplicadas à Demanda Turística
title Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand
spellingShingle Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand
Borges , Júnia Lúcio de Castro
TripAdvisor
Big Data
Tourist Demand
digital footprint
Tourism Planning
TripAdvisor
Big Data
Demanda Turística
pegadas digitais
Planejamento Turístico
title_short Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand
title_full Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand
title_fullStr Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand
title_full_unstemmed Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand
title_sort Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand
author Borges , Júnia Lúcio de Castro
author_facet Borges , Júnia Lúcio de Castro
Riani Costa Perinotto , André
de Souza Braga, Solano
author_role author
author2 Riani Costa Perinotto , André
de Souza Braga, Solano
author2_role author
author
dc.contributor.author.fl_str_mv Borges , Júnia Lúcio de Castro
Riani Costa Perinotto , André
de Souza Braga, Solano
dc.subject.por.fl_str_mv TripAdvisor
Big Data
Tourist Demand
digital footprint
Tourism Planning
TripAdvisor
Big Data
Demanda Turística
pegadas digitais
Planejamento Turístico
topic TripAdvisor
Big Data
Tourist Demand
digital footprint
Tourism Planning
TripAdvisor
Big Data
Demanda Turística
pegadas digitais
Planejamento Turístico
description This study applied text data mining techniques, statistical text analysis and network analysis in a qualitative database with information resulting from comments from social media specialized in tourism in order to identify the perception of visitors to the National Parks of Piauí. These conservation units are important tourist assets because they house a great natural heritage with extreme historical relevance with thousands of archaeological sites. The methodology developed in this article presents defensible and reproducible criteria to be replicated in any tourist attraction present on TripAdvisor, and greatly expands the understanding about the perception of visitors and the essence of places, facilitating the decision making of planners and managers of tourist destinations. The presented results were achieved from the use of automation techniques and computational programming to extract a large volume of data - Big Data - from digital footprints left by travelers on TripAdvisor, regarding the tourist attractions of interest. The qualitative database was mined using free software aimed at textual analysis, statistical treatment and network analysis. Based on the results, it was possible to identify key aspects regarding the tourist destinations such as the centrality of themes regarding archaeological aspects and the monitoring of local guides.
publishDate 2024
dc.date.none.fl_str_mv 2024-03-31
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://revistas.face.ufmg.br/index.php/mtr/article/view/8168
10.29149/mtr.v9i1.8168
url https://revistas.face.ufmg.br/index.php/mtr/article/view/8168
identifier_str_mv 10.29149/mtr.v9i1.8168
dc.language.iso.fl_str_mv por
eng
language por
eng
dc.relation.none.fl_str_mv https://revistas.face.ufmg.br/index.php/mtr/article/view/8168/4091
https://revistas.face.ufmg.br/index.php/mtr/article/view/8168/4092
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Federal University of Minas Gerais
publisher.none.fl_str_mv Federal University of Minas Gerais
dc.source.none.fl_str_mv Marketing & Tourism Review; Vol. 9 No. 1 (2024): v.9, n.1, 2024
Marketing & Tourism Review; v. 9 n. 1 (2024): v.9, n.1, 2024
2525-8176
2525-8176
10.29149/mtr.v9i1
reponame:Marketing & Tourism Review
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Marketing & Tourism Review
collection Marketing & Tourism Review
repository.name.fl_str_mv Marketing & Tourism Review - Universidade Federal de Minas Gerais (UFMG)
repository.mail.fl_str_mv mkt.tourism.review@gmail.com||mg.ufmg@gmail.com
_version_ 1798321033386655744

Registros relacionados