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    Ranking online consumer reviews

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    Rana_ECRA (749.5Kb)
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    Publication date
    2018-05
    Author
    Saumya, S.
    Singh, J.P.
    Baabdullah, A.M.
    Rana, Nripendra P.
    Dwivedi, Y.K.
    Keyword
    Big data challenge
    E-commerce
    Helpfulness
    Machine learning
    Online reviews
    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.
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    Product 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.
    URI
    http://hdl.handle.net/10454/18107
    Version
    Accepted manuscript
    Citation
    Saumya S, Singh JP, Baabdullah AM et al (2018) Ranking online consumer reviews. Electronic Commerce Research and Applications. 29: 78-89.
    Link to publisher’s version
    https://doi.org/10.1016/j.elerap.2018.03.008
    Type
    Article
    Collections
    Management and Law Publications

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