Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
Autor(a) principal: | |
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Data de Publicação: | 2017 |
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/10071/13321 |
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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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 |
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 |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
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 |
repository.mail.fl_str_mv |
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1799134684236480512 |