Profiling and Predicting the 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 fa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/10/10/295 |
| _version_ | 1828394020040081408 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-10T07:48:19Z |
| format | Article |
| id | doaj.art-5781f1e89e66497195de1b8b9b47d268 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-10T07:48:19Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-5781f1e89e66497195de1b8b9b47d2682022-12-22T01:57:07ZengMDPI AGInformation2078-24892019-09-01101029510.3390/info10100295info10100295Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced ReviewsMuhammad Bilal0Mohsen Marjani1Ibrahim Abaker Targio Hashem2Abdullah Gani3Misbah Liaqat4Kwangman Ko5School of Computing and IT, Taylor’s University, Subang Jaya 47500, MalaysiaSchool of Computing and IT, Taylor’s University, Subang Jaya 47500, MalaysiaSchool of Computing and IT, Taylor’s University, Subang Jaya 47500, MalaysiaDepartment of Computer System and Technology, University of Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Computer Science, Air University, Islamabad 44000, PakistanDepartment of Computer Engineering, Sangji University, Wonju 220-702, KoreaWith 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.https://www.mdpi.com/2078-2489/10/10/295review platformscrowd-sourced reviewsprofiling helpfulnessranking businesseshelpfulness prediction |
| spellingShingle | Muhammad Bilal Mohsen Marjani Ibrahim Abaker Targio Hashem Abdullah Gani Misbah Liaqat Kwangman Ko Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews Information review platforms crowd-sourced reviews profiling helpfulness ranking businesses helpfulness prediction |
| title | Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews |
| title_full | Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews |
| title_fullStr | Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews |
| title_full_unstemmed | Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews |
| title_short | Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews |
| title_sort | profiling and predicting the cumulative helpfulness quality of crowd sourced reviews |
| topic | review platforms crowd-sourced reviews profiling helpfulness ranking businesses helpfulness prediction |
| url | https://www.mdpi.com/2078-2489/10/10/295 |
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