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An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network

Ye Wang1, Bixin Liu2, Hongjia Wu1, Shan Zhao1, Zhiping Cai1, *, Donghui Li3, *, Cheang Chak Fong4

1 College of Computer, National University of Defense Technology, Changsha, 410073, China.
2 Academy of Military Science, Beijing, 100091, China.
3 College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China.
4 Faculty of Information Technology, Macau University of Science and Technology, 999078, Macau.

* Corresponding Author: Donghui Li. Email: email.

Computers, Materials & Continua 2020, 65(1), 355-367.


With the continuous development of e-commerce, consumers show increasing interest in posting comments on consumption experience and quality of commodities. Meanwhile, people make purchasing decisions relying on other comments much more than ever before. So the reliability of commodity comments has a significant impact on ensuring consumers’ equity and building a fair internet-trade-environment. However, some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits. Those improper ways of self-profiting have severely ruined the entire online shopping industry. Aiming to detect and prevent these deceptive comments effectively, we construct a model of Multi-Filters Convolutional Neural Network (MFCNN) for opinion spam detection. MFCNN is designed with a fixed-length sequence input and an improved activation function to avoid the gradient vanishing problem in spam opinion detection. Moreover, convolution filters with different widths are used in MFCNN to represent the sentences and documents. Our experimental results show that MFCNN outperforms current stateof-the-art methods on standard spam detection benchmarks.


Cite This Article

Y. Wang, B. Liu, H. Wu, S. Zhao, Z. Cai et al., "An opinion spam detection method based on multi-filters convolutional neural network," Computers, Materials & Continua, vol. 65, no.1, pp. 355–367, 2020.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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