Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniques

[EN] Consumers represent today a significant source of information to learn about products and services quality thanks to the proliferation of user-generated content in the form of online reviews. It is thus of paramount to understand what makes online reviews helpful to consumers as this evaluation...

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Detalles Bibliográficos
Autores: Olmedilla, Maria, Martinez-Torres, Rocio, Toral, Sergio L.
Tipo de recurso: capítulo de libro
Fecha de publicación:2018
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/112096
Acceso en línea:https://riunet.upv.es/handle/10251/112096
Access Level:acceso abierto
Palabra clave:Web data
Internet data
Big data
QCA
PLS
SEM
Conference
Text mining
Unique atributes
Objective and subjective appraisal
eWOM communities
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spelling Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniquesOlmedilla, MariaMartinez-Torres, RocioToral, Sergio L.Web dataInternet dataBig dataQCAPLSSEMConferenceText miningUnique atributesObjective and subjective appraisaleWOM communities[EN] Consumers represent today a significant source of information to learn about products and services quality thanks to the proliferation of user-generated content in the form of online reviews. It is thus of paramount to understand what makes online reviews helpful to consumers as this evaluation might affect their purchase decisions. In this regard, this research has applied textmining techniques by extracting the characteristics from online reviews' texts of an eWOM community, and further utilized these characteristics to train a logistic classifier using three classes: helpful, neutral and not helpful. The aim is identifying which unique attributes determine whether an online review is helpful or not. Findings reveal that there are much more unique attributes classified as helpful than attributes classified as not helpful. Additionally, the unique attributes associated to helpful reviews exhibit more objective appraisal while those associated to not helpful reviews show more subjective appraisal. The proposed methodology can be used to predict the helpfulness of posted reviews and to obtain their unique attributes.Editorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20182018-09-07book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/112096reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1120962026-06-13T07:49:27Z
dc.title.none.fl_str_mv Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniques
title Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniques
spellingShingle Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniques
Olmedilla, Maria
Web data
Internet data
Big data
QCA
PLS
SEM
Conference
Text mining
Unique atributes
Objective and subjective appraisal
eWOM communities
title_short Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniques
title_full Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniques
title_fullStr Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniques
title_full_unstemmed Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniques
title_sort Identification of helpful and not helpful online reviews within an eWOM community using text-mining techniques
dc.creator.none.fl_str_mv Olmedilla, Maria
Martinez-Torres, Rocio
Toral, Sergio L.
author Olmedilla, Maria
author_facet Olmedilla, Maria
Martinez-Torres, Rocio
Toral, Sergio L.
author_role author
author2 Martinez-Torres, Rocio
Toral, Sergio L.
author2_role author
author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Web data
Internet data
Big data
QCA
PLS
SEM
Conference
Text mining
Unique atributes
Objective and subjective appraisal
eWOM communities
topic Web data
Internet data
Big data
QCA
PLS
SEM
Conference
Text mining
Unique atributes
Objective and subjective appraisal
eWOM communities
description [EN] Consumers represent today a significant source of information to learn about products and services quality thanks to the proliferation of user-generated content in the form of online reviews. It is thus of paramount to understand what makes online reviews helpful to consumers as this evaluation might affect their purchase decisions. In this regard, this research has applied textmining techniques by extracting the characteristics from online reviews' texts of an eWOM community, and further utilized these characteristics to train a logistic classifier using three classes: helpful, neutral and not helpful. The aim is identifying which unique attributes determine whether an online review is helpful or not. Findings reveal that there are much more unique attributes classified as helpful than attributes classified as not helpful. Additionally, the unique attributes associated to helpful reviews exhibit more objective appraisal while those associated to not helpful reviews show more subjective appraisal. The proposed methodology can be used to predict the helpfulness of posted reviews and to obtain their unique attributes.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-09-07
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/112096
url https://riunet.upv.es/handle/10251/112096
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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