Sentiment analysis in online customer reviews: the Feels Like Home case
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/22965 |
Resumo: | Portugal has been, for many years, an attractive destination for tourists from all over the world. This continuous flow of people opens opportunities for companies to explore and for some new other companies to emerge. All the data generated from the interaction of these companies with tourists can be submitted to data mining techniques to extract useful information and, therefore, create knowledge.Thiscasestudy uses those data mining techniques to try to explain the polarity of sentiments found in the online reviews of the properties that Feels Like Home, a local accommodation rental platform, manages. Out of the Feels Like Home’s portfolio, information regarding negative and positive mentions for each house (monthly) was retrieved from ReviewPro’s API, allowing for the final data set to have 1131 entries containing important information to be targeted bydata mining.Through the usage of descriptive analysis and predictive models (CART decision trees), the relationship between the properties and reservations’ characteristics and the sentiment polarity found in the reviews is described, as well as the main factors that can help predict those sentiments are revealed. Additionally, the relationship between the monthly occupancy rates and the sentiments’ polarity is also described.This way, this study generates useful knowledge for Feels Like Home and possibly for the rest of the industry to use and adapt to their business needs |
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Sentiment analysis in online customer reviews: the Feels Like Home caseData mining -- Data miningCRISP-DMSentiment analysisPolarityRevMineração de dadosAnálise de sentimentosPolaridadeComentáriosPortugal has been, for many years, an attractive destination for tourists from all over the world. This continuous flow of people opens opportunities for companies to explore and for some new other companies to emerge. All the data generated from the interaction of these companies with tourists can be submitted to data mining techniques to extract useful information and, therefore, create knowledge.Thiscasestudy uses those data mining techniques to try to explain the polarity of sentiments found in the online reviews of the properties that Feels Like Home, a local accommodation rental platform, manages. Out of the Feels Like Home’s portfolio, information regarding negative and positive mentions for each house (monthly) was retrieved from ReviewPro’s API, allowing for the final data set to have 1131 entries containing important information to be targeted bydata mining.Through the usage of descriptive analysis and predictive models (CART decision trees), the relationship between the properties and reservations’ characteristics and the sentiment polarity found in the reviews is described, as well as the main factors that can help predict those sentiments are revealed. Additionally, the relationship between the monthly occupancy rates and the sentiments’ polarity is also described.This way, this study generates useful knowledge for Feels Like Home and possibly for the rest of the industry to use and adapt to their business needsPortugal tem sido, por muitos anos, um destino atraente para turistas vindos de todo o mundo. Este fluxo contínuo de pessoas abre oportunidades para empresas explorarem e para novas empresas surgirem. Toda a informação gerada a partir das interações entre estas empresas com turistas pode ser submetida a técnicas de data mining para poder extrair informação útil e, assim, gerar conhecimento.Este estudo de caso usa essas técnicas de data mining para tentar explicar a polaridade de sentimentos encontrada nos comentários online das propriedades que a Feels Like Home, uma plataforma de aluguer de alojamento local, gere. De todo o portfólio daFeels Like Home, informação acerca de menções negativas e positivas para cada casa (mensalmente) foi retirada da API daReviewPro, permitindo que a amostra final tivesse 1131 entradas contendo informação importante para ser alvo de data mining.Através do uso de análise descritiva e de modelos preditivos (árvores de decisão CART), a relação entre as características das propriedades e reservas e a polaridade dos sentimentos encontrada nos comentários é descrita, assim como os principais fatores que podem ajudar a prever esses sentimentos são revelados. Adicionalmente, a relação entre as taxas de ocupação mensais e a polaridade dos sentimentos é também descrita.Desta forma, este estudo gera conhecimento útil para a Feels Like Home e possivelmente para o resto da indústria poderem usar e adaptar às suas necessidades de negócio.2021-07-27T09:19:05Z2019-12-04T00:00:00Z2019-12-042019-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10071/22965TID:202746089engAlmeida, Duarte Rodrigues dos Santos Farinas deinfo: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:RCAAP2023-11-09T17:51:49Zoai:repositorio.iscte-iul.pt:10071/22965Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:25:43.626018Repositório 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 |
Sentiment analysis in online customer reviews: the Feels Like Home case |
title |
Sentiment analysis in online customer reviews: the Feels Like Home case |
spellingShingle |
Sentiment analysis in online customer reviews: the Feels Like Home case Almeida, Duarte Rodrigues dos Santos Farinas de Data mining -- Data mining CRISP-DM Sentiment analysis Polarity Rev Mineração de dados Análise de sentimentos Polaridade Comentários |
title_short |
Sentiment analysis in online customer reviews: the Feels Like Home case |
title_full |
Sentiment analysis in online customer reviews: the Feels Like Home case |
title_fullStr |
Sentiment analysis in online customer reviews: the Feels Like Home case |
title_full_unstemmed |
Sentiment analysis in online customer reviews: the Feels Like Home case |
title_sort |
Sentiment analysis in online customer reviews: the Feels Like Home case |
author |
Almeida, Duarte Rodrigues dos Santos Farinas de |
author_facet |
Almeida, Duarte Rodrigues dos Santos Farinas de |
author_role |
author |
dc.contributor.author.fl_str_mv |
Almeida, Duarte Rodrigues dos Santos Farinas de |
dc.subject.por.fl_str_mv |
Data mining -- Data mining CRISP-DM Sentiment analysis Polarity Rev Mineração de dados Análise de sentimentos Polaridade Comentários |
topic |
Data mining -- Data mining CRISP-DM Sentiment analysis Polarity Rev Mineração de dados Análise de sentimentos Polaridade Comentários |
description |
Portugal has been, for many years, an attractive destination for tourists from all over the world. This continuous flow of people opens opportunities for companies to explore and for some new other companies to emerge. All the data generated from the interaction of these companies with tourists can be submitted to data mining techniques to extract useful information and, therefore, create knowledge.Thiscasestudy uses those data mining techniques to try to explain the polarity of sentiments found in the online reviews of the properties that Feels Like Home, a local accommodation rental platform, manages. Out of the Feels Like Home’s portfolio, information regarding negative and positive mentions for each house (monthly) was retrieved from ReviewPro’s API, allowing for the final data set to have 1131 entries containing important information to be targeted bydata mining.Through the usage of descriptive analysis and predictive models (CART decision trees), the relationship between the properties and reservations’ characteristics and the sentiment polarity found in the reviews is described, as well as the main factors that can help predict those sentiments are revealed. Additionally, the relationship between the monthly occupancy rates and the sentiments’ polarity is also described.This way, this study generates useful knowledge for Feels Like Home and possibly for the rest of the industry to use and adapt to their business needs |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-12-04T00:00:00Z 2019-12-04 2019-10 2021-07-27T09:19:05Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/22965 TID:202746089 |
url |
http://hdl.handle.net/10071/22965 |
identifier_str_mv |
TID:202746089 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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.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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1799134820153950208 |