Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction

Du, Jiahua; Rong, Jackie; Wang, Hua; Zhang, Yanchun

doi:10.1007/978-3-030-34223-4_50

Cited by 18 publications

(12 citation statements)

References 36 publications

(41 reference statements)

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“…Moreover, it has been reported that different classification thresholds are needed for both types of products. A study captured the relationship between review rating and review content using deep neural networks and reported improved predictive performance of review helpfulness [79]. Liang et al [80] reported that more comprehensive reviews with extreme ratings are seen as helpful.…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

et al. 2020

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The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract people's attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation, linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R 2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers. INDEX TERMS Business reputation, rating behavior, review helpfulness, user profiling, crowd-sourced reviews.

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Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

et al. 2020

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“…Inspired by [12,51,68], TRI embeds star ratings to enlarge the encoding space for rating information. Different from [51], TRI decouples the representation learning of review texts from that of star ratings to avoid possible loss of rating information.…”

Section: Interaction Between Review Texts and Star Ratingsmentioning

confidence: 99%

“…The six largest domains are selected for evaluation, including Apps for Android, Video Games, Electronics, CDs and Vinyl, Movies and TV, and Books. A large number of online reviews ensure sufficient training data for the TRI (12)…”

Section: Datasetsmentioning

confidence: 99%

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

Zheng

et al. 2020

Data Sci. Eng.

Self Cite

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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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“…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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Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction

Cited by 18 publications

References 36 publications

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

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

Using Bayesian Network to Predict Online Review Helpfulness

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