A computational intelligence approach to efficiently predicting review ratings in e-commerce

Cosma, G ORCID: 0000-0002-4663-6907 and Acampora, G ORCID: 0000-0003-4082-5616, 2016. A computational intelligence approach to efficiently predicting review ratings in e-commerce. Applied Soft Computing, 44, pp. 153-162. ISSN 1568-4946

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Abstract

Sentiment analysis, also called opinion mining, is currently one of the most studied research fields which aims to analyse people's opinions. E-commerce websites allow users to share opinions about a product/service by providing textual reviews along with numerical ratings. These opinions greatly influence future consumer purchasing decisions. This paper introduces an innovative computational intelligence framework for efficiently predicting customer review ratings. The framework has been designed to deal with the dimensionality and noise which is typically apparent in large datasets containing customer reviews. The proposed framework integrates the techniques of Singular Value Decomposition (SVD) and dimensionality reduction, Fuzzy C-Means (FCM) and the Adaptive Neuro-Fuzzy Inference System (ANFIS). The performance of the proposed approach returned high accuracy and the results revealed that when large datasets are concerned, only a fraction of the data is needed for creating a system to predict the review ratings of textual reviews. Results from the experiments suggest that the proposed approach yields better prediction performance than other state-of-the-art rating predictors which are based on the conventional Artificial Neural Network, Fuzzy C-Means, and Support Vector Machine approaches. In addition, the proposed framework can be utilised for other classification and prediction tasks, and its neuro-fuzzy predictor module can be replaced by other classifiers.

Item Type: Journal article
Publication Title: Applied Soft Computing
Creators: Cosma, G. and Acampora, G.
Publisher: Elsevier
Date: July 2016
Volume: 44
ISSN: 1568-4946
Identifiers:
NumberType
10.1016/j.asoc.2016.02.024DOI
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 08 Apr 2016 10:12
Last Modified: 09 Jun 2017 14:01
URI: http://irep.ntu.ac.uk/id/eprint/27461

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