Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviews

[EN] Online reviews in the e-commerce and eWOM communities play a key role in consumers’ purchase decisions. In this regard, one concern is the growth of fake reviews, which directly targets the credibility of platforms and the trust of users. To address this issue, we apply Transformers-based NLP m...

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Detalles Bibliográficos
Autores: Olmedilla, María, Romero, José Carlos, Martínez-Torres, Rocío, Toral, Sergio
Tipo de recurso: capítulo de libro
Fecha de publicación:2023
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:10251/201708
Acceso en línea:https://riunet.upv.es/handle/10251/201708
Access Level:acceso abierto
Palabra clave:Online reviews
Transformers
GPT-2
BERT
Credibility
Verified purchase
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spelling Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviewsOlmedilla, MaríaRomero, José CarlosMartínez-Torres, RocíoToral, SergioOnline reviewsTransformersGPT-2BERTCredibilityVerified purchase[EN] Online reviews in the e-commerce and eWOM communities play a key role in consumers’ purchase decisions. In this regard, one concern is the growth of fake reviews, which directly targets the credibility of platforms and the trust of users. To address this issue, we apply Transformers-based NLP models to better understand the scope of fake reviews within the Amazon marketplace across different product categories. Our methodology applies two different transformer models to Amazon online reviews for (1) generating fake reviews and (2) classifying online reviews as fake or truthful. This work contributes to the literature on understanding the credibility of online review. Our results show that most of the fake reviews are located in non-verified purchase reviews. Considering the different product categories, we found that the percentage of fake reviews is 3 times higher for the experience products and 8 times higher for the experience products for non-verified purchase reviews with respect to the fake reviews found in verified-purchase reviews.This work was supported by the project Aplicación de Redes Generativas Antagónicas para Combatir la Manipulación de Clientes Online (REACT) Ref. PID2020-114527RB-I00 funded by MCIN/AEI/10.13039/501100011033Editorial Universitat Politècnica de ValènciaAgencia Estatal de InvestigaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-09-22book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/201708reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-114527RB-I00 APLICACION DE REDES GENERATIVAS ANTAGONICAS PARA COMBATIR LA MANIPULACION DE CLIENTES ONLINE (REACT)open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2017082026-06-13T07:49:27Z
dc.title.none.fl_str_mv Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviews
title Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviews
spellingShingle Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviews
Olmedilla, María
Online reviews
Transformers
GPT-2
BERT
Credibility
Verified purchase
title_short Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviews
title_full Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviews
title_fullStr Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviews
title_full_unstemmed Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviews
title_sort Applying Transformers-based NLP Models to Explore Credibility in Different Product Categories in Amazon’s online reviews
dc.creator.none.fl_str_mv Olmedilla, María
Romero, José Carlos
Martínez-Torres, Rocío
Toral, Sergio
author Olmedilla, María
author_facet Olmedilla, María
Romero, José Carlos
Martínez-Torres, Rocío
Toral, Sergio
author_role author
author2 Romero, José Carlos
Martínez-Torres, Rocío
Toral, Sergio
author2_role author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Online reviews
Transformers
GPT-2
BERT
Credibility
Verified purchase
topic Online reviews
Transformers
GPT-2
BERT
Credibility
Verified purchase
description [EN] Online reviews in the e-commerce and eWOM communities play a key role in consumers’ purchase decisions. In this regard, one concern is the growth of fake reviews, which directly targets the credibility of platforms and the trust of users. To address this issue, we apply Transformers-based NLP models to better understand the scope of fake reviews within the Amazon marketplace across different product categories. Our methodology applies two different transformer models to Amazon online reviews for (1) generating fake reviews and (2) classifying online reviews as fake or truthful. This work contributes to the literature on understanding the credibility of online review. Our results show that most of the fake reviews are located in non-verified purchase reviews. Considering the different product categories, we found that the percentage of fake reviews is 3 times higher for the experience products and 8 times higher for the experience products for non-verified purchase reviews with respect to the fake reviews found in verified-purchase reviews.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-09-22
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/10251/201708
url https://riunet.upv.es/handle/10251/201708
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-114527RB-I00 APLICACION DE REDES GENERATIVAS ANTAGONICAS PARA COMBATIR LA MANIPULACION DE CLIENTES ONLINE (REACT)
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/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 - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/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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