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Open AccessArticle

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

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School of Computing and IT, Taylor’s University, Subang Jaya 47500, Malaysia
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Centre for Data Science and Analytics (C4DSA), Taylor’s University, Subang Jaya 47500, Malaysia
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Department of Computer System and Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
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Faculty of Computing and Informatics, University Malaysia Sabah, Labuan International Campus, Labuan 87000, Malaysia
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Department of Computer Science, Air University, Islamabad 44000, Pakistan
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Department of Computer Engineering, Sangji University, Wonju 220-702, Korea
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Authors to whom correspondence should be addressed.
Information 2019, 10(10), 295; https://doi.org/10.3390/info10100295
Received: 27 August 2019 / Revised: 3 September 2019 / Accepted: 3 September 2019 / Published: 24 September 2019
(This article belongs to the Special Issue Big Data Analytics and Computational Intelligence)
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. View Full-Text
Keywords: review platforms; crowd-sourced reviews; profiling helpfulness; ranking businesses; helpfulness prediction review platforms; crowd-sourced reviews; profiling helpfulness; ranking businesses; helpfulness prediction
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Bilal, M.; Marjani, M.; Hashem, I.A.T.; Gani, A.; Liaqat, M.; Ko, K. Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews. Information 2019, 10, 295.

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