Context-Aware Helpfulness Prediction for Online Product Reviews

Olatunji, Iyiola E.; Li, Xin; Lam, Wai

doi:10.1007/978-3-030-42835-8_6

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…A higher R 2 was reported by the evaluations carried out using semantic measures compared to the existing vote-based assessments [69]. Olatunji et al [70] also argued that the ''X out of Y'' approach to assessing the quality of the reviews did not work well for reviews with fewer total votes. Therefore, a context-aware approach using textual features was proposed to predict the helpfulness of reviews.…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 97%

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: 97%

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

et al. 2020

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“…Zhang et al (2020) studied the problem of predicting the helpfulness of answers to users’ questions on specific product features based on a dual attention mechanism. Olatunji et al (2020) proposed a convolutional neural network with a context-aware encoding mechanism and pretrained language models to predict the product reviews’ helpfulness. Peer reviewing of student writing has been widely studied in education science.…”

Section: Related Workmentioning

confidence: 99%

Assessing and predicting the quality of peer reviews: a text mining approach

Meng

2023

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Purpose This paper aims to quantify the quality of peer reviews, evaluate them from different perspectives and develop a model to predict the review quality. In addition, this paper investigates effective features to distinguish the reviews' quality. Design/methodology/approach First, a fine-grained data set including peer review data, citations and review conformity scores was constructed. Second, metrics were proposed to evaluate the quality of peer reviews from three aspects. Third, five categories of features were proposed in terms of reviews, submissions and responses using natural language processing (NLP) techniques. Finally, different machine learning models were applied to predict the review quality, and feature analysis was performed to understand effective features. Findings The analysis results revealed that reviewers become more conservative and the review quality becomes worse over time in terms of these indicators. Among the three models, random forest model achieves the best performance on all three tasks. Sentiment polarity, review length, response length and readability are important factors that distinguish peer reviews’ quality, which can help meta-reviewers value more worthy reviews when making final decisions. Originality/value This study provides a new perspective for assessing review quality. Another originality of the research lies in the proposal of a novelty task that predict review quality. To address this task, a novel model was proposed which incorporated various of feature sets, thereby deepening the understanding of peer reviews.

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“…Recent studies using data mining techniques in various online product review analyses include applying data mining models to predict review helpfulness [16,17,[21][22][23]. Various BI methods are employed such as k-nearest neighbors (kNN) [24], inductive learning based on rough set theory [25], ordinal logistic regression analysis [21], backpropagation neural networks [26], a neural deep learning model [27][28][29][30], support vector regression [31], and content analysis to assess text comments using logistic regression [18].…”

Section: Research Backgroundmentioning

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-Aware Helpfulness Prediction for Online Product Reviews

Cited by 7 publications

References 24 publications

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

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

Assessing and predicting the quality of peer reviews: a text mining approach

Using Bayesian Network to Predict Online Review Helpfulness

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