Review helpfulness prediction: Survey

Almutairi, Yasamyian; Abdullah, Manal; Alahmadi, Dimah

doi:10.21533/pen.v7i1.420

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Context features are those extracted from outside the review, such as reviewer information. (Ocampo Diaz and Ng, 2018;Almutairi et al, 2019;Arif et al, 2018). Most of these features are used in domains such as products, books, hotels and so on.…”

Section: Featuresmentioning

confidence: 99%

Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese

Sousa¹,

Pardo²

2022

Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment &Amp; Social Media Analysis

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Over the years, the review helpfulness prediction task has been the subject of several works, but remains being a challenging issue in Natural Language Processing, as results vary a lot depending on the domain, on the adopted features and on the chosen classification strategy. This paper attempts to evaluate the impact of content features and classification methods for two different domains. In particular, we run our experiments for a low resource language -Portuguese -, trying to establish a benchmark for this language. We show that simple features and classical classification methods are powerful for the task of helpfulness prediction, but are largely outperformed by a convolutional neural network-based solution.

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Section: Featuresmentioning

confidence: 99%

Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese

Sousa¹,

Pardo²

2022

Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment &Amp; Social Media Analysis

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“…While there exist a large number of studies on review helpfulness prediction, the rationale regarding specific selection of features is still vague and needs further research [34][35][36][37]. As this study intends to encompass various aspects of reviews beyond text, our study categorizes various factors affecting review usefulness according to Baek et al [38], Filieri [11], Filieri et al [39], Ghose and Ipeirotis [17], and Lee and Cheoh [26].…”

Section: Determinants Of Helpfulnessmentioning

confidence: 99%

Using Bayesian Network to Predict Online Review Helpfulness

Lee

Choeh

2020

Sustainability

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The enormous volume and largely varying quality of available reviews provide a great obstacle to seek out the most helpful reviews. While Naive Bayesian Network (NBN) is one of the matured artificial intelligence approaches for business decision support, the usage of NBN to predict the helpfulness of online reviews is lacking. This study intends to suggest HPNBN (a helpfulness prediction model using NBN), which adopts NBN for helpfulness prediction. This study crawled sample data from Amazon website and 8699 reviews comprise the final sample. Twenty-one predictors represent reviewer and textual traits as well as product traits of the reviews. We investigate how the expanded list of predictors including product, reviewer, and textual characteristics of eWOM (online word-of-mouth) has an effect on helpfulness by suggesting conditional probabilities of the binned determinants. The prediction accuracy of NBN outperformed that of the k-nearest neighbor (kNN) method and the neural network (NN) model. The results of this study can support determining helpfulness and support website design to induce review helpfulness. This study will help decision-makers predict the helpfulness of the review comments posted to their websites and manage more effective customer satisfaction strategies. When prospect customers feel such review helpfulness, they will have a stronger intention to pay a regular visit to the target website.

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“…Context features are those extracted from outside the review, such as reviewer information. (Ocampo Almutairi et al, 2019;Arif et al, 2018). Most of these features are used in domains such as products, books, hotels and so on.…”

Section: Featuresmentioning

confidence: 99%

Classificação da utilidade de opiniões em português brasileiro

Sousa

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Em primeiro lugar agradeço a Deus, pois Ele me deu vida e capacidade durante toda essa jornada. Sem a graça dEle eu nada poderia fazer.Sou muito grato também à minha amada esposa, Milena, por todo carinho e atenção durante esses anos, e pelo seu amor incondicional ao me acompanhar para outra cidade até então desconhecida e sempre estar ao meu lado, independentemente da situação. A minha princesinha linda Yasmin, "produção" do doutorado, mesmo pequenininha, entendeu os momentos em que eu não podia dar a atenção que ela merece. A Serena que, no momento em que escrevo estas palavras, ainda está no "forninho", pois ela já teve participação indispensável nesse fim de doutorado.Agradeço aos meus pais, Rubem e Rosa, que dispensaram incontáveis esforços para que eu pudesse ter uma boa educação dentro e fora de casa. Além de proverem todos os recursos necessários para meu crescimento saudável, tanto físico quanto mental e por serem meus exemplos de vida. Aos meus irmãos, Rafael, Rebeca e Rubem Filho pela amizade e companheirismo que só eles poderiam me proporcionar.Aos meus professores da Universidade Federal do Piauí, especialmente ao Prof. Dr. Raimundo Santos Moura, por aceitar me orientar como aluno de iniciação científica, trabalho de conclusão de curso e de mestrado e me acompanhado com excelentes conselhos e ensinamentos que me ajudaram em todas as etapas da minha vida acadêmica.Aos meus queridos orientadores, Profa. Dra. Maria das Graças Volpe Nunes e Prof. Dr Thiago Alexandre Salgueiro Pardo. A professora Graça foi fundamental no início do doutorado, pelos seus valiosos conselhos e conhecimentos compartilhados; sempre atenciosa e disposta a ajudar para me encaminhar para os melhores caminhos. Ao professor Thiago, por aceitar ser meu orientador, sempre com paciência e zelo. Da mesma forma, agradeço pela parceria, ensinamentos e pelos muitos conhecimentos que compartilhamos. Sem os dois, eu não teria chegado até esse momento, ambos são incríveis e são exemplos a serem seguidos, pessoal e profissionalmente.

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Review helpfulness prediction: Survey

Cited by 6 publications

References 40 publications

Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese

Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese

Using Bayesian Network to Predict Online Review Helpfulness

Classificação da utilidade de opiniões em português brasileiro

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