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dc.contributor.authorSaumya, S.
dc.contributor.authorSingh, J.P.
dc.contributor.authorBaabdullah, A.M.
dc.contributor.authorRana, Nripendra P.
dc.contributor.authorDwivedi, Y.K.
dc.date.accessioned2020-09-26T09:49:24Z
dc.date.accessioned2020-10-12T10:22:13Z
dc.date.available2020-09-26T09:49:24Z
dc.date.available2020-10-12T10:22:13Z
dc.date.issued2018-05
dc.identifier.citationSaumya S, Singh JP, Baabdullah AM et al (2018) Ranking online consumer reviews. Electronic Commerce Research and Applications. 29: 78-89.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18107
dc.descriptionYesen_US
dc.description.abstractProduct reviews are posted online by the hundreds and thousands for popular products. Handling such a large volume of continuously generated online content is a challenging task for buyers, sellers and researchers. The purpose of this study is to rank the overwhelming number of reviews using their predicted helpfulness scores. The helpfulness score is predicted using features extracted from review text, product description, and customer question-answer data of a product using the random-forest classifier and gradient boosting regressor. The system classifies reviews into low or high quality with the random-forest classifier. The helpfulness scores of the high-quality reviews are only predicted using the gradient boosting regressor. The helpfulness scores of the low-quality reviews are not calculated because they are never going to be in the top k reviews. They are just added at the end of the review list to the review-listing website. The proposed system provides fair review placement on review listing pages and makes all high-quality reviews visible to customers on the top. The experimental results on data from two popular Indian e-commerce websites validate our claim, as 3–4 newer high-quality reviews are placed in the top ten reviews along with 5–6 older reviews based on review helpfulness. Our findings indicate that inclusion of features from product description data and customer question-answer data improves the prediction accuracy of the helpfulness score.en_US
dc.description.sponsorshipMinistry of Electronics and Information Technology (MeitY), Government of India for financial support during research work through “Visvesvaraya PhD Scheme for Electronics and IT”.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.elerap.2018.03.008en_US
dc.rights© 2018 Elsevier B.V. All rights reserved. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.en_US
dc.subjectBig data challengeen_US
dc.subjectE-commerceen_US
dc.subjectHelpfulnessen_US
dc.subjectMachine learningen_US
dc.subjectOnline reviewsen_US
dc.titleRanking online consumer reviewsen_US
dc.status.refereedYesen_US
dc.date.Accepted2018-03-28
dc.date.application2018-03-29
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.date.updated2020-09-26T08:49:26Z
refterms.dateFOA2020-10-12T10:22:49Z


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