Business Analytics in Tourism: Uncovering Knowledge from Crowds
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | BAR - Brazilian Administration Review |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-76922019000200305 |
Resumo: | Abstract Business Analytics leverages value from data, thus being an important tool for the decision-making process. However, the presence of data in different formats is a new challenge for analysis. Textual data has been drawing organizational attention as thousands of people express themselves daily in text, like the description of customer perceptions in the tourism and hospitality area. Despite the relevance of customer data in textual format to support decision making of hotel managers, its use is still modest, given the difficulty of analyzing and interpreting the large amounts of data. Our objective is to identify the main evaluation topics presented in online guest reviews and reveal changes throughout the years. We worked with 23,229 hotel reviews collected from TripAdvisor website through WebScrapping packages in R, and used a text mining approach (Latent Semantic Analysis) to analyze the data. This contributes with practical implications to hotel managers by demonstrating the applicability of text data and tools based on open-source solutions and by providing insights about the data and assisting in the decision-making process. This article also contributes in presenting a stepwise text analysis, including capturing, cleaning and formatting publicly available data for organizational specialists. |
id |
ANPAD-1_da9e8fd9536d61bd4e51cabaea967174 |
---|---|
oai_identifier_str |
oai:scielo:S1807-76922019000200305 |
network_acronym_str |
ANPAD-1 |
network_name_str |
BAR - Brazilian Administration Review |
repository_id_str |
|
spelling |
Business Analytics in Tourism: Uncovering Knowledge from Crowdstext miningbusiness analyticshotel reviewsAbstract Business Analytics leverages value from data, thus being an important tool for the decision-making process. However, the presence of data in different formats is a new challenge for analysis. Textual data has been drawing organizational attention as thousands of people express themselves daily in text, like the description of customer perceptions in the tourism and hospitality area. Despite the relevance of customer data in textual format to support decision making of hotel managers, its use is still modest, given the difficulty of analyzing and interpreting the large amounts of data. Our objective is to identify the main evaluation topics presented in online guest reviews and reveal changes throughout the years. We worked with 23,229 hotel reviews collected from TripAdvisor website through WebScrapping packages in R, and used a text mining approach (Latent Semantic Analysis) to analyze the data. This contributes with practical implications to hotel managers by demonstrating the applicability of text data and tools based on open-source solutions and by providing insights about the data and assisting in the decision-making process. This article also contributes in presenting a stepwise text analysis, including capturing, cleaning and formatting publicly available data for organizational specialists.ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-76922019000200305BAR - Brazilian Administration Review v.16 n.2 2019reponame:BAR - Brazilian Administration Reviewinstname:Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD)instacron:ANPAD10.1590/1807-7692bar2019180136info:eu-repo/semantics/openAccessMarcolin,CarlaBecker,João LuizWild,FridolinSchiavi,GiovanaBehr,Arieleng2019-08-06T00:00:00Zoai:scielo:S1807-76922019000200305Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=1807-7692&lng=pt&nrm=isohttps://old.scielo.br/oai/scielo-oai.php||bar@anpad.org.br1807-76921807-7692opendoar:2019-08-06T00:00BAR - Brazilian Administration Review - Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD)false |
dc.title.none.fl_str_mv |
Business Analytics in Tourism: Uncovering Knowledge from Crowds |
title |
Business Analytics in Tourism: Uncovering Knowledge from Crowds |
spellingShingle |
Business Analytics in Tourism: Uncovering Knowledge from Crowds Marcolin,Carla text mining business analytics hotel reviews |
title_short |
Business Analytics in Tourism: Uncovering Knowledge from Crowds |
title_full |
Business Analytics in Tourism: Uncovering Knowledge from Crowds |
title_fullStr |
Business Analytics in Tourism: Uncovering Knowledge from Crowds |
title_full_unstemmed |
Business Analytics in Tourism: Uncovering Knowledge from Crowds |
title_sort |
Business Analytics in Tourism: Uncovering Knowledge from Crowds |
author |
Marcolin,Carla |
author_facet |
Marcolin,Carla Becker,João Luiz Wild,Fridolin Schiavi,Giovana Behr,Ariel |
author_role |
author |
author2 |
Becker,João Luiz Wild,Fridolin Schiavi,Giovana Behr,Ariel |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Marcolin,Carla Becker,João Luiz Wild,Fridolin Schiavi,Giovana Behr,Ariel |
dc.subject.por.fl_str_mv |
text mining business analytics hotel reviews |
topic |
text mining business analytics hotel reviews |
description |
Abstract Business Analytics leverages value from data, thus being an important tool for the decision-making process. However, the presence of data in different formats is a new challenge for analysis. Textual data has been drawing organizational attention as thousands of people express themselves daily in text, like the description of customer perceptions in the tourism and hospitality area. Despite the relevance of customer data in textual format to support decision making of hotel managers, its use is still modest, given the difficulty of analyzing and interpreting the large amounts of data. Our objective is to identify the main evaluation topics presented in online guest reviews and reveal changes throughout the years. We worked with 23,229 hotel reviews collected from TripAdvisor website through WebScrapping packages in R, and used a text mining approach (Latent Semantic Analysis) to analyze the data. This contributes with practical implications to hotel managers by demonstrating the applicability of text data and tools based on open-source solutions and by providing insights about the data and assisting in the decision-making process. This article also contributes in presenting a stepwise text analysis, including capturing, cleaning and formatting publicly available data for organizational specialists. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-76922019000200305 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-76922019000200305 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1807-7692bar2019180136 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração |
publisher.none.fl_str_mv |
ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração |
dc.source.none.fl_str_mv |
BAR - Brazilian Administration Review v.16 n.2 2019 reponame:BAR - Brazilian Administration Review instname:Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD) instacron:ANPAD |
instname_str |
Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD) |
instacron_str |
ANPAD |
institution |
ANPAD |
reponame_str |
BAR - Brazilian Administration Review |
collection |
BAR - Brazilian Administration Review |
repository.name.fl_str_mv |
BAR - Brazilian Administration Review - Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD) |
repository.mail.fl_str_mv |
||bar@anpad.org.br |
_version_ |
1754209124166402048 |