Predicting the helpfulness of online product reviews: A multilingual approach

Zhang, Ying; Lin, Zhijie

doi:10.1016/j.elerap.2017.10.008

Cited by 75 publications

(58 citation statements)

References 44 publications

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“…The dataset of 4248 non-English reviews was collected from Yelp.com. The previously identified features related to review content, business and reviewer were analyzed using regression, i.e., LNR, and classification techniques, i.e., SVM [21]. The analysis of scripts for predicting review helpfulness was performed with the help of human annotators that highlight the important phrases that make a review helpful.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The linear regression did not show better performance as reported by a few studies. However, it is still the most widely adopted for the task of helpfulness prediction due to its fast execution time and explanatory power when compared with other methods [21,25,[57][58][59][60][61][62]. To validate the proposed models, 10-fold cross-validation was used.…”

Section: Modeling and Evaluation Metricsmentioning

confidence: 99%

“…The topic of predicting helpfulness of reviews has been studied by many researchers using similar features but reported inconsistent and contradictory results regarding the performance of different features in predicting helpfulness [20]. Most of the solutions introduced by previous studies were for a specific category, product or platform [21,22]. Researchers have tried to propose a generalized solution for different review platforms and product categories by using only textual features in making the prediction.…”

Section: Introductionmentioning

confidence: 99%

“…However, they also suggested utilizing reviewer and product features to enhance the prediction performance [23]. The datasets used for predicting helpfulness by previous studies are mostly different, small size and overdispersed [21,24]. Diaz and Ng [7] highlighted the disorganized status of research in the area of review helpfulness prediction.…”

Section: Introductionmentioning

confidence: 99%

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Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews

et al. 2019

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With easy access to the Internet and the popularity of online review platforms, the volume of crowd-sourced reviews is continuously rising. Many studies have acknowledged the importance of reviews in making purchase decisions. The consumer’s feedback plays a vital role in the success or failure of a business. The number of studies on predicting helpfulness and ranking reviews is increasing due to the increasing importance of reviews. However, previous studies have mainly focused on predicting helpfulness of “reviews” and “reviewer”. This study aimed to profile cumulative helpfulness received by a business and then use it for business ranking. The reliability of proposed cumulative helpfulness for ranking was illustrated using a dataset of 1,92,606 businesses from Yelp.com. Seven business and four reviewer features were identified to predict cumulative helpfulness using Linear Regression (LNR), Gradient Boosting (GB), and Neural Network (NNet). The dataset was subdivided into 12 datasets based on business categories to predict the cumulative helpfulness. The results reported that business features, including star rating, review count and days since the last review are the most important features among all business categories. Moreover, using reviewer features along with business features improves the prediction performance for seven datasets. Lastly, the implications of this study are discussed for researchers, review platforms and businesses.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Modeling and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews

et al. 2019

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“…In addition, the researchers chose top 100 reviews who reviewed the most times. The papers [28] collected a huge numbers of reviews and reviews relevant information, such as review contents, reviewer information and product details . The dataset offered by Yelp.com, it is collected between October 2004 and December 2015.…”

Section: Datasets Used In Review Helpfulness Predictionmentioning

confidence: 99%

Review helpfulness prediction: Survey

Almutairi¹,

Abdullah²,

Alahmadi³

2019

PEN

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Online reviews have become the major driving factor influencing purchasing behavior and patterns of social customers. However, it is difficult for customer to cover good reviews about any product or service according to massive amount of reviews latest years. Many previous researches provide innovative models about predicting review helpfulness in E-commerce websites. Some of these studies exploring the direct effect of review attributes on review helpfulness while others focused on reviewer's attributes only. The main objective of this research is to review the most important attributes that have an affect on review helpfulness from many perspectives such as datasets, techniques, frameworks and evaluation methods of the experiments. The paper ends up with important findings about most attributes effect the review helpfulness such as Review Valence.

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