Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
Autor(a) principal: | |
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Data de Publicação: | 2018 |
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/1822/62743 |
Resumo: | Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors' affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena. |
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Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysisBig dataMarketingLiterature analysisResearch trendsText miningSocial SciencesM15M31Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors' affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena.Elsevier Science LtdUniversidade do MinhoAmado, AlexandraCortez, PauloRita, PauloMoro, Sergio20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/62743eng2444-883410.1016/j.iedeen.2017.06.002https://www.sciencedirect.com/science/article/pii/S2444883417300268info: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:RCAAP2023-07-21T12:22:05Zoai:repositorium.sdum.uminho.pt:1822/62743Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:15:31.677418Repositório 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 |
Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis |
title |
Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis |
spellingShingle |
Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis Amado, Alexandra Big data Marketing Literature analysis Research trends Text mining Social Sciences M15 M31 |
title_short |
Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis |
title_full |
Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis |
title_fullStr |
Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis |
title_full_unstemmed |
Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis |
title_sort |
Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis |
author |
Amado, Alexandra |
author_facet |
Amado, Alexandra Cortez, Paulo Rita, Paulo Moro, Sergio |
author_role |
author |
author2 |
Cortez, Paulo Rita, Paulo Moro, Sergio |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Amado, Alexandra Cortez, Paulo Rita, Paulo Moro, Sergio |
dc.subject.por.fl_str_mv |
Big data Marketing Literature analysis Research trends Text mining Social Sciences M15 M31 |
topic |
Big data Marketing Literature analysis Research trends Text mining Social Sciences M15 M31 |
description |
Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors' affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2018-01-01T00:00:00Z |
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/1822/62743 |
url |
http://hdl.handle.net/1822/62743 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2444-8834 10.1016/j.iedeen.2017.06.002 https://www.sciencedirect.com/science/article/pii/S2444883417300268 |
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 Science Ltd |
publisher.none.fl_str_mv |
Elsevier Science Ltd |
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 |
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1799132601534906368 |