Examining Airbnb guest satisfaction tendencies: A text mining approach
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
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/10071/26724 |
Resumo: | Given Airbnb's changes since its inception and the dynamism of customer preferences, a study that sheds light on how customer satisfaction is evolving is relevant. An automated method is proposed for identifying these satisfaction tendencies at a large scale. This study follows a text mining approach to analyse 590,070 reviews posted between 2010 and 2019 on the Airbnb platform in Lisbon. Topic Modelling is employed in order to identify the main topics discussed in the reviews, and Sentiment Analysis to understand the topics that compose guest’s satisfaction in the context of Airbnb services. Three major topics are extracted from Airbnb reviews: ‘host’s service’, ‘physical aspects’, and ‘location’. Although a positivity bias in guest reviews is confirmed, the satisfaction level seems to be decreasing over the years. The results also reveal that ‘physical aspects’ is the predominant topic when considering the negative guest reviews. This research considers big data the base to create knowledge, data spanning over the years, offering consistency to the research. |
id |
RCAP_4451a1aa3af3a3edcc5b2db0200a3106 |
---|---|
oai_identifier_str |
oai:repositorio.iscte-iul.pt:10071/26724 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Examining Airbnb guest satisfaction tendencies: A text mining approachAirbnbOnline reviewsTopic modellingSentiment analysisSatisfactionHospitalityGiven Airbnb's changes since its inception and the dynamism of customer preferences, a study that sheds light on how customer satisfaction is evolving is relevant. An automated method is proposed for identifying these satisfaction tendencies at a large scale. This study follows a text mining approach to analyse 590,070 reviews posted between 2010 and 2019 on the Airbnb platform in Lisbon. Topic Modelling is employed in order to identify the main topics discussed in the reviews, and Sentiment Analysis to understand the topics that compose guest’s satisfaction in the context of Airbnb services. Three major topics are extracted from Airbnb reviews: ‘host’s service’, ‘physical aspects’, and ‘location’. Although a positivity bias in guest reviews is confirmed, the satisfaction level seems to be decreasing over the years. The results also reveal that ‘physical aspects’ is the predominant topic when considering the negative guest reviews. This research considers big data the base to create knowledge, data spanning over the years, offering consistency to the research.Routledge/Taylor and Francis2024-02-29T00:00:00Z2022-01-01T00:00:00Z20222022-12-21T12:12:19Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/26724eng1368-350010.1080/13683500.2022.2115877Santos, M.Ribeiro, R.Batista, F.Correia, A.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:RCAAP2024-03-03T01:16:16Zoai:repositorio.iscte-iul.pt:10071/26724Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:14:15.928578Repositório 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 |
Examining Airbnb guest satisfaction tendencies: A text mining approach |
title |
Examining Airbnb guest satisfaction tendencies: A text mining approach |
spellingShingle |
Examining Airbnb guest satisfaction tendencies: A text mining approach Santos, M. Airbnb Online reviews Topic modelling Sentiment analysis Satisfaction Hospitality |
title_short |
Examining Airbnb guest satisfaction tendencies: A text mining approach |
title_full |
Examining Airbnb guest satisfaction tendencies: A text mining approach |
title_fullStr |
Examining Airbnb guest satisfaction tendencies: A text mining approach |
title_full_unstemmed |
Examining Airbnb guest satisfaction tendencies: A text mining approach |
title_sort |
Examining Airbnb guest satisfaction tendencies: A text mining approach |
author |
Santos, M. |
author_facet |
Santos, M. Ribeiro, R. Batista, F. Correia, A. |
author_role |
author |
author2 |
Ribeiro, R. Batista, F. Correia, A. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Santos, M. Ribeiro, R. Batista, F. Correia, A. |
dc.subject.por.fl_str_mv |
Airbnb Online reviews Topic modelling Sentiment analysis Satisfaction Hospitality |
topic |
Airbnb Online reviews Topic modelling Sentiment analysis Satisfaction Hospitality |
description |
Given Airbnb's changes since its inception and the dynamism of customer preferences, a study that sheds light on how customer satisfaction is evolving is relevant. An automated method is proposed for identifying these satisfaction tendencies at a large scale. This study follows a text mining approach to analyse 590,070 reviews posted between 2010 and 2019 on the Airbnb platform in Lisbon. Topic Modelling is employed in order to identify the main topics discussed in the reviews, and Sentiment Analysis to understand the topics that compose guest’s satisfaction in the context of Airbnb services. Three major topics are extracted from Airbnb reviews: ‘host’s service’, ‘physical aspects’, and ‘location’. Although a positivity bias in guest reviews is confirmed, the satisfaction level seems to be decreasing over the years. The results also reveal that ‘physical aspects’ is the predominant topic when considering the negative guest reviews. This research considers big data the base to create knowledge, data spanning over the years, offering consistency to the research. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01T00:00:00Z 2022 2022-12-21T12:12:19Z 2024-02-29T00:00:00Z |
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/10071/26724 |
url |
http://hdl.handle.net/10071/26724 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1368-3500 10.1080/13683500.2022.2115877 |
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 |
Routledge/Taylor and Francis |
publisher.none.fl_str_mv |
Routledge/Taylor and Francis |
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 |
|
_version_ |
1799134699378966528 |