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Deceptive reviews detection of industrial product

Deceptive reviews detection of industrial product Deceptive reviews of products can greatly swing the customer's purchasing decisions. We propose a new method to decrease the influence of deceptive reviews on industrial products by improving the of detecting these reviews. The method recognises the deceptive reviews based on the posters' behaviours and the reviews' content. It firstly builds a recognition model of the `water army' according to the review's quantity, frequency and length, and then builds the content model with five reviews' content features, i.e. the length, the degree of professionalism, the emotional density, the format and the emotional imbalance, and finally detects the deceptive reviews of industrial products by combining an unsupervised clustering algorithm based on F statistics and a feature degree. Our method achieves better results than existing ones according to tests on industrial products of automobiles, mobile phones and computers. Its is better than that of identification methods based only on content feature clustering. Keywords: deceptive reviews detection; behaviour feature; content feature; industrial product. Reference to this paper should be made as follows: Deng, S. (2016) `Deceptive reviews detection of industrial product', Int. J. Services Operations and Informatics, Vol. 8, No. 2, pp.122­135. Biographical notes: Song Deng received his PhD degree in http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Services Operations and Informatics Inderscience Publishers

Deceptive reviews detection of industrial product

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Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1741-539X
eISSN
1741-5403
DOI
10.1504/IJSOI.2016.080090
Publisher site
See Article on Publisher Site

Abstract

Deceptive reviews of products can greatly swing the customer's purchasing decisions. We propose a new method to decrease the influence of deceptive reviews on industrial products by improving the of detecting these reviews. The method recognises the deceptive reviews based on the posters' behaviours and the reviews' content. It firstly builds a recognition model of the `water army' according to the review's quantity, frequency and length, and then builds the content model with five reviews' content features, i.e. the length, the degree of professionalism, the emotional density, the format and the emotional imbalance, and finally detects the deceptive reviews of industrial products by combining an unsupervised clustering algorithm based on F statistics and a feature degree. Our method achieves better results than existing ones according to tests on industrial products of automobiles, mobile phones and computers. Its is better than that of identification methods based only on content feature clustering. Keywords: deceptive reviews detection; behaviour feature; content feature; industrial product. Reference to this paper should be made as follows: Deng, S. (2016) `Deceptive reviews detection of industrial product', Int. J. Services Operations and Informatics, Vol. 8, No. 2, pp.122­135. Biographical notes: Song Deng received his PhD degree in

Journal

International Journal of Services Operations and InformaticsInderscience Publishers

Published: Jan 1, 2016

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