Unfolding the characteristics of incentivized online reviews

Detalhes bibliográficos
Autor(a) principal: Costa, A.
Data de Publicação: 2019
Outros Autores: Guerreiro, J., Moro, S., Henriques, R.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/17102
Resumo: The rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. Since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely true. Therefore, review systems emerged to provide more trustworthy sources of information, since customer opinions may be less biased. However, the need to control the consumers’ opinion increased once sellers realized the importance of reviews and their direct impact on sales. One of the methods often used was to offer customers a specific product in exchange for an honest review. Yet, these incentivized reviews bias results and skew the overall rating of the products. The current study uses a data mining approach to predict whether or not a new review published was incentivized based on several review features such as the overall rating, the helpfulness rate, and the review length, among others. Additionally, the model was enriched with sentiment score features of the reviews computed through the VADER algorithm. The results provide an in-depth understanding of the phenomenon by identifying the most relevant features which enable to differentiate an incentivized from a non-incentivized review, thus providing users and companies with a simple set of rules to identify reviews that are biased without any disclaimer. Such rules include the length of a review, its helpfulness rate, and the overall sentiment polarity score.
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spelling Unfolding the characteristics of incentivized online reviewsIncentivized online reviewsText miningSentiment analysisThe rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. Since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely true. Therefore, review systems emerged to provide more trustworthy sources of information, since customer opinions may be less biased. However, the need to control the consumers’ opinion increased once sellers realized the importance of reviews and their direct impact on sales. One of the methods often used was to offer customers a specific product in exchange for an honest review. Yet, these incentivized reviews bias results and skew the overall rating of the products. The current study uses a data mining approach to predict whether or not a new review published was incentivized based on several review features such as the overall rating, the helpfulness rate, and the review length, among others. Additionally, the model was enriched with sentiment score features of the reviews computed through the VADER algorithm. The results provide an in-depth understanding of the phenomenon by identifying the most relevant features which enable to differentiate an incentivized from a non-incentivized review, thus providing users and companies with a simple set of rules to identify reviews that are biased without any disclaimer. Such rules include the length of a review, its helpfulness rate, and the overall sentiment polarity score.Elsevier2019-01-21T13:27:45Z2020-01-21T00:00:00Z2019-01-01T00:00:00Z20192019-01-21T13:27:13Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/17102eng0969-698910.1016/j.jretconser.2018.12.006Costa, A.Guerreiro, J.Moro, S.Henriques, R.info: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:RCAAP2023-11-09T17:44:46Zoai:repositorio.iscte-iul.pt:10071/17102Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:21:17.523390Repositório 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 Unfolding the characteristics of incentivized online reviews
title Unfolding the characteristics of incentivized online reviews
spellingShingle Unfolding the characteristics of incentivized online reviews
Costa, A.
Incentivized online reviews
Text mining
Sentiment analysis
title_short Unfolding the characteristics of incentivized online reviews
title_full Unfolding the characteristics of incentivized online reviews
title_fullStr Unfolding the characteristics of incentivized online reviews
title_full_unstemmed Unfolding the characteristics of incentivized online reviews
title_sort Unfolding the characteristics of incentivized online reviews
author Costa, A.
author_facet Costa, A.
Guerreiro, J.
Moro, S.
Henriques, R.
author_role author
author2 Guerreiro, J.
Moro, S.
Henriques, R.
author2_role author
author
author
dc.contributor.author.fl_str_mv Costa, A.
Guerreiro, J.
Moro, S.
Henriques, R.
dc.subject.por.fl_str_mv Incentivized online reviews
Text mining
Sentiment analysis
topic Incentivized online reviews
Text mining
Sentiment analysis
description The rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. Since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely true. Therefore, review systems emerged to provide more trustworthy sources of information, since customer opinions may be less biased. However, the need to control the consumers’ opinion increased once sellers realized the importance of reviews and their direct impact on sales. One of the methods often used was to offer customers a specific product in exchange for an honest review. Yet, these incentivized reviews bias results and skew the overall rating of the products. The current study uses a data mining approach to predict whether or not a new review published was incentivized based on several review features such as the overall rating, the helpfulness rate, and the review length, among others. Additionally, the model was enriched with sentiment score features of the reviews computed through the VADER algorithm. The results provide an in-depth understanding of the phenomenon by identifying the most relevant features which enable to differentiate an incentivized from a non-incentivized review, thus providing users and companies with a simple set of rules to identify reviews that are biased without any disclaimer. Such rules include the length of a review, its helpfulness rate, and the overall sentiment polarity score.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-21T13:27:45Z
2019-01-01T00:00:00Z
2019
2019-01-21T13:27:13Z
2020-01-21T00:00:00Z
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language eng
dc.relation.none.fl_str_mv 0969-6989
10.1016/j.jretconser.2018.12.006
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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