Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks

Autores
Sá, Carlos Augusto de; Moura, Raimundo S.
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Author reputation is a very important variable for evaluating web comments. However, there is no formal definition for calculating its value. This paper presents an adaptation of the approach presented by Sousa (2015) for evaluating the importance of comments about products and services available online, emphasizing measures of author reputation. The implemented adaptation consists in defining six measures for authors, used as input in a Multilayer Perceptron Artificial Neural Network. On a preliminary evaluation, the Neural Network presented an accuracy of 91.01% on the author classification process. Additionally, an experiment was conduced aiming to compare both approaches, and the results show that the adapted approach had better performance in classifying the importance of comments.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
author reputation
artificial neural networks
fuzzy systems
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/63207
Acceder
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network_name_str SEDICI (UNLP)
spelling Approach to define Author Reputation in Web Product Reviews using Artificial Neural NetworksSá, Carlos Augusto deMoura, Raimundo S.Ciencias Informáticasauthor reputationartificial neural networksfuzzy systemsAuthor reputation is a very important variable for evaluating web comments. However, there is no formal definition for calculating its value. This paper presents an adaptation of the approach presented by Sousa (2015) for evaluating the importance of comments about products and services available online, emphasizing measures of author reputation. The implemented adaptation consists in defining six measures for authors, used as input in a Multilayer Perceptron Artificial Neural Network. On a preliminary evaluation, the Neural Network presented an accuracy of 91.01% on the author classification process. Additionally, an experiment was conduced aiming to compare both approaches, and the results show that the adapted approach had better performance in classifying the importance of comments.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2017-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/63207enginfo:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/SLMDI/SLMDI-06.pdfinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:00:48Zoai:sedici.unlp.edu.ar:10915/63207Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:00:48.805SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks
title Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks
spellingShingle Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks
Sá, Carlos Augusto de
Ciencias Informáticas
author reputation
artificial neural networks
fuzzy systems
title_short Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks
title_full Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks
title_fullStr Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks
title_full_unstemmed Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks
title_sort Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks
dc.creator.none.fl_str_mv Sá, Carlos Augusto de
Moura, Raimundo S.
author Sá, Carlos Augusto de
author_facet Sá, Carlos Augusto de
Moura, Raimundo S.
author_role author
author2 Moura, Raimundo S.
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
author reputation
artificial neural networks
fuzzy systems
topic Ciencias Informáticas
author reputation
artificial neural networks
fuzzy systems
dc.description.none.fl_txt_mv Author reputation is a very important variable for evaluating web comments. However, there is no formal definition for calculating its value. This paper presents an adaptation of the approach presented by Sousa (2015) for evaluating the importance of comments about products and services available online, emphasizing measures of author reputation. The implemented adaptation consists in defining six measures for authors, used as input in a Multilayer Perceptron Artificial Neural Network. On a preliminary evaluation, the Neural Network presented an accuracy of 91.01% on the author classification process. Additionally, an experiment was conduced aiming to compare both approaches, and the results show that the adapted approach had better performance in classifying the importance of comments.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description Author reputation is a very important variable for evaluating web comments. However, there is no formal definition for calculating its value. This paper presents an adaptation of the approach presented by Sousa (2015) for evaluating the importance of comments about products and services available online, emphasizing measures of author reputation. The implemented adaptation consists in defining six measures for authors, used as input in a Multilayer Perceptron Artificial Neural Network. On a preliminary evaluation, the Neural Network presented an accuracy of 91.01% on the author classification process. Additionally, an experiment was conduced aiming to compare both approaches, and the results show that the adapted approach had better performance in classifying the importance of comments.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/63207
url http://sedici.unlp.edu.ar/handle/10915/63207
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/SLMDI/SLMDI-06.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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