Can we trace back hotel online reviews’ characteristics using gamification features?
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/16674 |
Resumo: | Gamification is here to stay, and tourism and hospitality online review platforms are taking advantage of it to attract travelers and motivate them to contribute to their websites. Yet, literature in tourism is scarce in studying how effectively is users’ behavior changing through gamification features. This research aims at filling such gap through a data-driven approach based on a large volume of online reviews (a total of 67,685) collected from TripAdvisor between 2016 and 2017. Four artificial neural networks were trained to model title and review's word length, and title and review's sentiment score, using as input 12 gamification features used in TripAdvisor including points and badges. After validating the accuracy of the model for extracting knowledge, the data-based sensitivity analysis was applied to understand how each of the 12 features contributed to explaining review length and its sentiment score. Three badge features were considered the most relevant ones, including the total number of badges, the passport badges, and the explorer badges, providing evidence of a relation between gamification features and traveler's behavior when writing reviews. |
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Can we trace back hotel online reviews’ characteristics using gamification features?GamificationHospitalityHotelsNeural networksOnline reviewsSentiment analysisGamification is here to stay, and tourism and hospitality online review platforms are taking advantage of it to attract travelers and motivate them to contribute to their websites. Yet, literature in tourism is scarce in studying how effectively is users’ behavior changing through gamification features. This research aims at filling such gap through a data-driven approach based on a large volume of online reviews (a total of 67,685) collected from TripAdvisor between 2016 and 2017. Four artificial neural networks were trained to model title and review's word length, and title and review's sentiment score, using as input 12 gamification features used in TripAdvisor including points and badges. After validating the accuracy of the model for extracting knowledge, the data-based sensitivity analysis was applied to understand how each of the 12 features contributed to explaining review length and its sentiment score. Three badge features were considered the most relevant ones, including the total number of badges, the passport badges, and the explorer badges, providing evidence of a relation between gamification features and traveler's behavior when writing reviews.Elsevier2018-10-15T15:33:15Z2019-10-15T00:00:00Z2019-01-01T00:00:00Z20192018-10-15T16:31:03Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/16674eng0268-401210.1016/j.ijinfomgt.2018.09.015Moro, S.Ramos, P.Esmerado, J.Jalali, S. M. J.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:47:51Zoai:repositorio.iscte-iul.pt:10071/16674Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:23:16.449197Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Can we trace back hotel online reviews’ characteristics using gamification features? |
title |
Can we trace back hotel online reviews’ characteristics using gamification features? |
spellingShingle |
Can we trace back hotel online reviews’ characteristics using gamification features? Moro, S. Gamification Hospitality Hotels Neural networks Online reviews Sentiment analysis |
title_short |
Can we trace back hotel online reviews’ characteristics using gamification features? |
title_full |
Can we trace back hotel online reviews’ characteristics using gamification features? |
title_fullStr |
Can we trace back hotel online reviews’ characteristics using gamification features? |
title_full_unstemmed |
Can we trace back hotel online reviews’ characteristics using gamification features? |
title_sort |
Can we trace back hotel online reviews’ characteristics using gamification features? |
author |
Moro, S. |
author_facet |
Moro, S. Ramos, P. Esmerado, J. Jalali, S. M. J. |
author_role |
author |
author2 |
Ramos, P. Esmerado, J. Jalali, S. M. J. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Moro, S. Ramos, P. Esmerado, J. Jalali, S. M. J. |
dc.subject.por.fl_str_mv |
Gamification Hospitality Hotels Neural networks Online reviews Sentiment analysis |
topic |
Gamification Hospitality Hotels Neural networks Online reviews Sentiment analysis |
description |
Gamification is here to stay, and tourism and hospitality online review platforms are taking advantage of it to attract travelers and motivate them to contribute to their websites. Yet, literature in tourism is scarce in studying how effectively is users’ behavior changing through gamification features. This research aims at filling such gap through a data-driven approach based on a large volume of online reviews (a total of 67,685) collected from TripAdvisor between 2016 and 2017. Four artificial neural networks were trained to model title and review's word length, and title and review's sentiment score, using as input 12 gamification features used in TripAdvisor including points and badges. After validating the accuracy of the model for extracting knowledge, the data-based sensitivity analysis was applied to understand how each of the 12 features contributed to explaining review length and its sentiment score. Three badge features were considered the most relevant ones, including the total number of badges, the passport badges, and the explorer badges, providing evidence of a relation between gamification features and traveler's behavior when writing reviews. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-10-15T15:33:15Z 2018-10-15T16:31:03Z 2019-10-15T00:00:00Z 2019-01-01T00:00:00Z 2019 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/16674 |
url |
http://hdl.handle.net/10071/16674 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0268-4012 10.1016/j.ijinfomgt.2018.09.015 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134794544578560 |