Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip

Detalhes bibliográficos
Autor(a) principal: Moro, S.
Data de Publicação: 2017
Outros Autores: Rita, P., Coelho, J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/13321
Resumo: This study presents a data mining approach for modeling TripAdvisor score using 504 reviews published in 2015 for the 21 hotels located in the Strip, Las Vegas. Nineteen quantitative features characterizing the reviews, hotels and the users were prepared and used for feeding a support vector machine for modeling the score. The results achieved reveal the model demonstrated adequate predictive performance. Therefore, a sensitivity analysis was applied over the model for extracting useful knowledge translated into features' relevance for the score. The findings unveiled user features related to TripAdvisor membership experience play a key role in influencing the scores granted, clearly surpassing hotel features. Also, both seasonality and the day of the week were found to influence scores. Such knowledge may be helpful in directing efforts to answer online reviews in alignment with hotel strategies, by profiling the reviews according to the member and review date.
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spelling Stripping customers' feedback on hotels through data mining: the case of Las Vegas StripCustomer feedbackCustomer reviewsOnline reviewsKnowledge extractionData miningModelingSensitivity analysisLas VegasThis study presents a data mining approach for modeling TripAdvisor score using 504 reviews published in 2015 for the 21 hotels located in the Strip, Las Vegas. Nineteen quantitative features characterizing the reviews, hotels and the users were prepared and used for feeding a support vector machine for modeling the score. The results achieved reveal the model demonstrated adequate predictive performance. Therefore, a sensitivity analysis was applied over the model for extracting useful knowledge translated into features' relevance for the score. The findings unveiled user features related to TripAdvisor membership experience play a key role in influencing the scores granted, clearly surpassing hotel features. Also, both seasonality and the day of the week were found to influence scores. Such knowledge may be helpful in directing efforts to answer online reviews in alignment with hotel strategies, by profiling the reviews according to the member and review date.Elsevier2017-05-12T10:01:29Z2017-01-01T00:00:00Z20172019-03-29T15:47:39Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/13321eng2211-973610.1016/j.tmp.2017.04.003Moro, S.Rita, P.Coelho, 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:RCAAP2023-11-09T17:28:43Zoai:repositorio.iscte-iul.pt:10071/13321Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:12:52.342966Repositório 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 Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
title Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
spellingShingle Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
Moro, S.
Customer feedback
Customer reviews
Online reviews
Knowledge extraction
Data mining
Modeling
Sensitivity analysis
Las Vegas
title_short Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
title_full Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
title_fullStr Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
title_full_unstemmed Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
title_sort Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
author Moro, S.
author_facet Moro, S.
Rita, P.
Coelho, J.
author_role author
author2 Rita, P.
Coelho, J.
author2_role author
author
dc.contributor.author.fl_str_mv Moro, S.
Rita, P.
Coelho, J.
dc.subject.por.fl_str_mv Customer feedback
Customer reviews
Online reviews
Knowledge extraction
Data mining
Modeling
Sensitivity analysis
Las Vegas
topic Customer feedback
Customer reviews
Online reviews
Knowledge extraction
Data mining
Modeling
Sensitivity analysis
Las Vegas
description This study presents a data mining approach for modeling TripAdvisor score using 504 reviews published in 2015 for the 21 hotels located in the Strip, Las Vegas. Nineteen quantitative features characterizing the reviews, hotels and the users were prepared and used for feeding a support vector machine for modeling the score. The results achieved reveal the model demonstrated adequate predictive performance. Therefore, a sensitivity analysis was applied over the model for extracting useful knowledge translated into features' relevance for the score. The findings unveiled user features related to TripAdvisor membership experience play a key role in influencing the scores granted, clearly surpassing hotel features. Also, both seasonality and the day of the week were found to influence scores. Such knowledge may be helpful in directing efforts to answer online reviews in alignment with hotel strategies, by profiling the reviews according to the member and review date.
publishDate 2017
dc.date.none.fl_str_mv 2017-05-12T10:01:29Z
2017-01-01T00:00:00Z
2017
2019-03-29T15:47:39Z
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/10071/13321
url http://hdl.handle.net/10071/13321
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2211-9736
10.1016/j.tmp.2017.04.003
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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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
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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