Title: Fuzzy-based review rating prediction in e-commerce

Authors: P. Velvizhy; A. Pravi; M. Selvi; S. Ganapathy; A. Kannan

Addresses: Department of Computer Science and Engineering, CEG Campus, Anna University, Chennai-600025, Tamil Nadu, India ' Department of Computer Science and Engineering, CEG Campus, Anna University, Chennai-600025, Tamil Nadu, India ' Department of Information Science and Technology, CEG Campus, Anna University, Chennai-600025, Tamil Nadu, India ' School of Computing Science and Engineering, VIT-Chennai Campus, Chennai-600127, Tamil Nadu, India ' Department of Information Science and Technology, CEG Campus, Anna University, Chennai-600025, Tamil Nadu, India

Abstract: Opinion mining is an ongoing research area in e-commerce which aims at analyzing the people's opinions, sentiments and emotions. Moreover, the existing e-commerce systems allow the users to share their feedback in the form of textual reviews regarding the products and services. It also allows the consumers to give ratings for products that help in future recommendation of products. In this research work, a computational framework for efficiently predicting the consumer review ratings on the products has been proposed. The proposed framework integrates dimensionality reduction, genetic algorithm, fuzzy c-means and adaptive neuro-fuzzy inference techniques to overcome the limitations of the existing systems. Experiments have been conducted in this work using Amazon dataset consisting of reviews for different products. This system provides better performance and prediction accuracy for review ratings when it is compared with the related work.

Keywords: sentiment analysis; review ratings prediction; dimensionality reduction; genetic algorithm; data mining; fuzzy c-means.

DOI: 10.1504/IJBIDM.2020.108034

International Journal of Business Intelligence and Data Mining, 2020 Vol.17 No.1, pp.101 - 116

Received: 23 Jun 2017
Accepted: 22 Dec 2017

Published online: 05 Apr 2020 *

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