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

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

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...

Full description

Saved in:

Bibliographic Details
Main Authors:	Muhammad Bilal, Mohsen Marjani, Ibrahim Abaker Targio Hashem, Abdullah Gani, Misbah Liaqat, Kwangman Ko
Format:	Article
Language:	English
Published:	Multidisciplinary Digital Publishing Institute (MDPI) 2019
Subjects:	QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware
Online Access:	https://eprints.ums.edu.my/id/eprint/42491/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42491/ http://dx.doi.org/10.3390/info10100295
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.ums.eprints.42491
record_format	eprints
spelling	my.ums.eprints.424912024-12-31T03:18:08Z https://eprints.ums.edu.my/id/eprint/42491/ Profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews Muhammad Bilal Mohsen Marjani Ibrahim Abaker Targio Hashem Abdullah Gani Misbah Liaqat Kwangman Ko QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware 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. Multidisciplinary Digital Publishing Institute (MDPI) 2019 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42491/1/FULL%20TEXT.pdf 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. http://dx.doi.org/10.3390/info10100295
institution	Universiti Malaysia Sabah
building	UMS Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sabah
content_source	UMS Institutional Repository
url_provider	http://eprints.ums.edu.my/
language	English
topic	QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware
spellingShingle	QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware Muhammad Bilal Mohsen Marjani Ibrahim Abaker Targio Hashem Abdullah Gani Misbah Liaqat Kwangman Ko Profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews
description	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.
format	Article
author	Muhammad Bilal Mohsen Marjani Ibrahim Abaker Targio Hashem Abdullah Gani Misbah Liaqat Kwangman Ko
author_facet	Muhammad Bilal Mohsen Marjani Ibrahim Abaker Targio Hashem Abdullah Gani Misbah Liaqat Kwangman Ko
author_sort	Muhammad Bilal
title	Profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews
title_short	Profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews
title_full	Profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews
title_fullStr	Profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews
title_full_unstemmed	Profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews
title_sort	profiling and predicting t1he cumulative helpfulness (quality) of crowd-sourced reviews
publisher	Multidisciplinary Digital Publishing Institute (MDPI)
publishDate	2019
url	https://eprints.ums.edu.my/id/eprint/42491/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42491/ http://dx.doi.org/10.3390/info10100295
_version_	1821003224455315456
score	13.226497

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

Similar Items