Crowdsourcing, Big Data and Network analysis techniques applied to Tourist Demand
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Outros Autores: | , |
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. |
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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 |