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Predicting the “helpfulness” of online consumer reviews
Singh, J.P. Irani, S. Rana, Nripendra P. Dwivedi, Y.K. Saumya, S. Kumar Roy, P.
Singh, J.P.
Irani, S.
Rana, Nripendra P.
Dwivedi, Y.K.
Saumya, S.
Kumar Roy, P.
Publication Date
2017-01
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Rights
© 2017 Elsevier. 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 (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
03/08/2016
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Department
Awarded
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Abstract
Online shopping is increasingly becoming people's first choice when shopping, as it is very convenient to choose products based on their reviews. Even for moderately popular products, there are thousands of reviews constantly being posted on e-commerce sites. Such a large volume of data constantly being generated can be considered as a big data challenge for both online businesses and consumers. That makes it difficult for buyers to go through all the reviews to make purchase decisions. In this research, we have developed models based on machine learning that can predict the helpfulness of the consumer reviews using several textual features such as polarity, subjectivity, entropy, and reading ease. The model will automatically assign helpfulness values to an initial review as soon as it is posted on the website so that the review gets a fair chance of being viewed by other buyers. The results of this study will help buyers to write better reviews and thereby assist other buyers in making their purchase decisions, as well as help businesses to improve their websites.
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Accepted manuscript
Citation
Singh JP, Irani S, Rana NP et al (2017) Predicting the “helpfulness” of online consumer reviews. Journal of Business Research. 70: 346-355.
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Article