Pairwise Reviews Ranking and Classification for Medicine E-Commerce Application
Author
Uppal, Shaurya
Jayal, Ambikesh
Arora, Anuja
Date
2019-09-19Acceptance date
2019-08-08
Type
Article
Publisher
IEEE
ISSN
2572-6129
Metadata
Show full item recordAbstract
E-Commerce applications provide an added advantage to customer to buy product with added suggestions in the form of reviews. Obviously, reviews are useful and impactful for customers those are going to a buy product. But these enormous amount of reviews create problem also for customers as they are not able to segregate useful ones. Therefore, there is a need for an approach which will showcase only relevant reviews to the customers. This same problem has been attempted in this research paper as this is a less explored area. Pairwise Review relevance ranking method is proposed in this research paper. This approach will sort reviews based on their relevance with the product and avoid showing irrelevant reviews. This work has been done in three phases- feature extraction, pairwise review ranking, and classification. The outcome is sorted list of reviews, review ranking accuracy and classification accuracy. Four classifiers- SVM, Random forest, Neural network, and logistic regression have been applied to validate ranking accuracy. Out of all four applied classification models, Random forest gives the best result. our proposed system is able to achieve 99.76% classification accuracy and 99.56% ranking accuracy for a complete dataset using random forest.
Journal/conference proceeding
2019 Twelfth International Conference on Contemporary Computing (IC3);
Citation
Uppal, S., Jayal, A. and Arora, A. (2019) 'Pairwise Reviews Ranking and Classification for Medicine E-Commerce Application', 2019 Twelfth International Conference on Contemporary Computing (IC3), Noida, India, 8-10 August. DOI: 10.1109/IC3.2019.8844887.
Description
Article presented at 2019 Twelfth International Conference on Contemporary Computing (IC3), available online on 19 September 2019, available at: https://doi.org/10.1109/IC3.2019.8844887.
Sponsorship
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
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