Profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews

Muhammad Bilal and Mohsen Marjani and Ibrahim Abaker Targio Hashem and Abdullah Gani and Misbah Liaqat and Kwangman Ko (2019) Profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews. Information, 10. pp. 1-21.

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Abstract

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.

Item Type:	Article
Keyword:	Review platforms, Crowd-sourced reviews, Profiling helpfulness, Ranking businesses, Helpfulness prediction
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	31 Dec 2024 11:18
Last Modified:	31 Dec 2024 11:18
URI:	https://eprints.ums.edu.my/id/eprint/42491

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