Modeling and Prediction of Online Product Review Helpfulness: A Survey

Diaz, Gerardo Ocampo; Ng, Vincent

doi:10.18653/v1/p18-1065

Cited by 43 publications

(18 citation statements)

References 43 publications

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“…Various models [17,25,35,38,64] have been proposed to identify helpful reviews. The mainstream solution [44] is to design feature patterns from review texts, review metadata, and social networks of reviewers. Such methods, albeit effective, are often product-and domain-dependent.…”

Section: Content-based Helpfulness Predictionmentioning

confidence: 99%

“…The past decade has seen a large body of studies [2, 22,44] relying on both review texts and star ratings for helpfulness prediction. In most of the feature engineering approaches [5,6,19,23], rating information is used in conjunction with review texts by concatenating learned content representations and raw rating values.…”

Section: Interaction Between Review Texts and Star Ratingsmentioning

confidence: 99%

“…Helpfulness prediction aims to identify and recommend high-quality reviews to customers in an automatic manner. The previous literature [2, 22,44] largely employs review texts and star ratings for the task. The rationale lies in their ubiquitousness in contemporary online shopping platforms and their importance to review helpfulness modeling.…”

Section: Introductionmentioning

confidence: 99%

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An Interactive Network for End-to-End Review Helpfulness Modeling

Zheng

et al. 2020

Data Sci. Eng.

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Review helpfulness prediction aims to prioritize online reviews by quality. Existing methods largely combine review texts and star ratings for helpfulness prediction. However, star ratings are used in a way that has either little representation capacity or limited interaction with review texts. As a result, rating information has yet to be fully exploited during the combination. This paper aims to overcome the two drawbacks. A deep interactive architecture is proposed to learn the text-rating interaction (TRI) for helpfulness modeling. TRI enlarges the representation capacity of star ratings while enhancing the influence of rating information on review texts. TRI is evaluated on six real-world domains of the Amazon 5-Core dataset. Extensive experiments demonstrate that TRI can better predict review helpfulness and beat the state of the art. Ablation studies and qualitative analysis are provided to further understand model behaviors and the learned parameters.

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Section: Content-based Helpfulness Predictionmentioning

confidence: 99%

Section: Interaction Between Review Texts and Star Ratingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Interactive Network for End-to-End Review Helpfulness Modeling

Zheng

et al. 2020

Data Sci. Eng.

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“…Previous studies have concentrated on mining useful features from the content (i. e., the review itself) and/or the context (other sources such as reviewer or user information) of the reviews [6,10,13,15,[18][19][20]25,27,28].…”

Section: Related Workmentioning

confidence: 99%

An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction

Farkas

et al. 2020

Lecture Notes in Computer Science

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Many people browse reviews online before making purchasing decisions. It is essential to identify the subset of helpful reviews from the large number of reviews of varying quality. This paper aims to build a model to predict review helpfulness automatically. Our work is inspired by the observation that a customer's expectation of a review can be greatly affected by review sentiment and the degree to which the customer is aware of pertinent product information. Consequently, a customer may pay more attention to that specific content of a review which contributes more to its helpfulness from their perspective. To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. Specifically, we encode the sentiment of a review through an attention module, to get sentiment-driven information from review text. We also introduce a product attention layer that fuses information from both the target product and related products, in order to capture the product related information from review text. Our experimental results show an AUC improvement of 5.4% and 1.5% over the previous state of the art model on Amazon and Yelp data sets, respectively.

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“…The motivation behind the “helpful” voting is a social mechanism for online communities to evaluate opinions of each other and mitigate noise in reviews (Ghose & Ipeirotis, ; Otterbacher, ). Although the “helpful” vote by an individual is a personal decision and can be influenced by many factors (Ocampo Diaz & Ng, ), collectively the “helpful” votes reflect the quality of reviews (Otterbacher, ; Otterbacher, Hemphill, & Dekker, ). Review helpfulness votes have been used to compute reviewer reputation (Otterbacher, ) as well as to evaluate review quality (Ghose & Ipeirotis, ).…”

Section: Introductionmentioning

confidence: 99%

Beyond mean rating: Probabilistic aggregation of star ratings based on helpfulness

Tay

Zhang

Karimi

2019

Asso for Info Science & Tech

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The star-rating mechanism of customer reviews is used universally by the online population to compare and select merchants, movies, products, and services. The consensus opinion from aggregation of star ratings is used as a proxy for item quality. Online reviews are noisy and effective aggregation of star ratings to accurately reflect the "true quality" of products and services is challenging. The mean-rating aggregation model is widely used and other aggregation models are also proposed. These existing aggregation models rely on a large number of reviews to tolerate noise. However, many products rarely have reviews. We propose probabilistic aggregation models for review ratings based on the Dirichlet distribution to combat data sparsity in reviews. We further propose to exploit the "helpfulness" social information and time to filter noisy reviews and effectively aggregate ratings to compute the consensus opinion. Our experiments on an Amazon data set show that our probabilistic aggregation models based on "helpfulness" achieve better performance than the statistical and heuristic baseline approaches.

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Modeling and Prediction of Online Product Review Helpfulness: A Survey

Cited by 43 publications

References 43 publications

An Interactive Network for End-to-End Review Helpfulness Modeling

An Interactive Network for End-to-End Review Helpfulness Modeling

An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction

Beyond mean rating: Probabilistic aggregation of star ratings based on helpfulness

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