Open Access
ARTICLE
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: .
Computers, Materials & Continua 2020, 65(1), 355-367. https://doi.org/10.32604/cmc.2020.09835
Received 21 January 2020; Accepted 11 May 2020; Issue published 23 July 2020
Abstract
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.
Keywords
Cite This Article
APA Style
Wang, Y., Liu, B., Wu, H., Zhao, S., Cai, Z. et al. (2020). An opinion spam detection method based on multi-filters convolutional neural network. Computers, Materials & Continua, 65(1), 355-367. https://doi.org/10.32604/cmc.2020.09835
Vancouver Style
Wang Y, Liu B, Wu H, Zhao S, Cai Z, Li D, et al. An opinion spam detection method based on multi-filters convolutional neural network. Comput Mater Contin. 2020;65(1):355-367 https://doi.org/10.32604/cmc.2020.09835
IEEE Style
Y. Wang et al., "An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network," Comput. Mater. Contin., vol. 65, no. 1, pp. 355-367. 2020. https://doi.org/10.32604/cmc.2020.09835