{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:48:38Z","timestamp":1761598118208,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T00:00:00Z","timestamp":1569283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2018090561, 2017030223"],"award-info":[{"award-number":["2018090561, 2017030223"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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\u2019s 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 \u201creviews\u201d and \u201creviewer\u201d. 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.<\/jats:p>","DOI":"10.3390\/info10100295","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T03:51:18Z","timestamp":1569383478000},"page":"295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9827-5023","authenticated-orcid":false,"given":"Muhammad","family":"Bilal","sequence":"first","affiliation":[{"name":"School of Computing and IT, Taylor\u2019s University, Subang Jaya 47500, Malaysia"},{"name":"Centre for Data Science and Analytics (C4DSA), Taylor\u2019s University, Subang Jaya 47500, Malaysia"}]},{"given":"Mohsen","family":"Marjani","sequence":"additional","affiliation":[{"name":"School of Computing and IT, Taylor\u2019s University, Subang Jaya 47500, Malaysia"},{"name":"Centre for Data Science and Analytics (C4DSA), Taylor\u2019s University, Subang Jaya 47500, Malaysia"}]},{"given":"Ibrahim Abaker Targio","family":"Hashem","sequence":"additional","affiliation":[{"name":"School of Computing and IT, Taylor\u2019s University, Subang Jaya 47500, Malaysia"},{"name":"Centre for Data Science and Analytics (C4DSA), Taylor\u2019s University, Subang Jaya 47500, Malaysia"}]},{"given":"Abdullah","family":"Gani","sequence":"additional","affiliation":[{"name":"Department of Computer System and Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"},{"name":"Faculty of Computing and Informatics, University Malaysia Sabah, Labuan International Campus, Labuan 87000, Malaysia"}]},{"given":"Misbah","family":"Liaqat","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Air University, Islamabad 44000, Pakistan"}]},{"given":"Kwangman","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Sangji University, Wonju 220-702, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TKDE.2010.188","article-title":"Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics","volume":"23","author":"Ghose","year":"2010","journal-title":"IEEE Trans. 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