Sentiment Analysis of Twitter in Tourism Destinations

[EN] Given the importance of electronic word of mouth (eWOM), this paper analyses the content of messages generated by users related to a tourist destination and shared through Twitter. We propose three research questions regarding eWOM behaviour in Twitter focused on the expertise of the reviewer,...

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Detalhes bibliográficos
Autores: Perez Cabañero, Carmen, Bigne, Enrique, Ruiz Mafe, Carla, Cuenca, Antonio Carlos
Formato: capítulo de livro
Fecha de publicación:2020
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/148826
Acesso em linha:https://riunet.upv.es/handle/10251/148826
Access Level:acceso abierto
Palavra-chave:Web data
Internet data
Big data
Qca
Pls
Sem
Conference
Twitter
eWOM
Tourism
Sentiment analysis
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spelling Sentiment Analysis of Twitter in Tourism DestinationsPerez Cabañero, CarmenBigne, EnriqueRuiz Mafe, CarlaCuenca, Antonio CarlosWeb dataInternet dataBig dataQcaPlsSemConferenceTwittereWOMTourismSentiment analysis[EN] Given the importance of electronic word of mouth (eWOM), this paper analyses the content of messages generated by users related to a tourist destination and shared through Twitter. We propose three research questions regarding eWOM behaviour in Twitter focused on the expertise of the reviewer, sentiment analysis of a tweet and its content.In order to address those research questions we carry out text mining analysis by retrieving existing information on Twitter (over 1500 tweets) regarding to Venice as a tourist destination.Authors acknowledge financial support of research project UV-INV_AE19-1212255.Editorial Universitat Politècnica de ValènciaUniversitat de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20202020-05-12book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/148826reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengUniversitat de València https://doi.org/10.13039/100008457 AE19-1212255open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1488262026-06-13T07:49:27Z
dc.title.none.fl_str_mv Sentiment Analysis of Twitter in Tourism Destinations
title Sentiment Analysis of Twitter in Tourism Destinations
spellingShingle Sentiment Analysis of Twitter in Tourism Destinations
Perez Cabañero, Carmen
Web data
Internet data
Big data
Qca
Pls
Sem
Conference
Twitter
eWOM
Tourism
Sentiment analysis
title_short Sentiment Analysis of Twitter in Tourism Destinations
title_full Sentiment Analysis of Twitter in Tourism Destinations
title_fullStr Sentiment Analysis of Twitter in Tourism Destinations
title_full_unstemmed Sentiment Analysis of Twitter in Tourism Destinations
title_sort Sentiment Analysis of Twitter in Tourism Destinations
dc.creator.none.fl_str_mv Perez Cabañero, Carmen
Bigne, Enrique
Ruiz Mafe, Carla
Cuenca, Antonio Carlos
author Perez Cabañero, Carmen
author_facet Perez Cabañero, Carmen
Bigne, Enrique
Ruiz Mafe, Carla
Cuenca, Antonio Carlos
author_role author
author2 Bigne, Enrique
Ruiz Mafe, Carla
Cuenca, Antonio Carlos
author2_role author
author
author
dc.contributor.none.fl_str_mv Universitat de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Web data
Internet data
Big data
Qca
Pls
Sem
Conference
Twitter
eWOM
Tourism
Sentiment analysis
topic Web data
Internet data
Big data
Qca
Pls
Sem
Conference
Twitter
eWOM
Tourism
Sentiment analysis
description [EN] Given the importance of electronic word of mouth (eWOM), this paper analyses the content of messages generated by users related to a tourist destination and shared through Twitter. We propose three research questions regarding eWOM behaviour in Twitter focused on the expertise of the reviewer, sentiment analysis of a tweet and its content.In order to address those research questions we carry out text mining analysis by retrieving existing information on Twitter (over 1500 tweets) regarding to Venice as a tourist destination.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-05-12
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/148826
url https://riunet.upv.es/handle/10251/148826
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Universitat de València https://doi.org/10.13039/100008457 AE19-1212255
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869415416226381824
score 15,301603

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