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An opinion spam detection method based on multi-filters convolutional neural network

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成果类型:
期刊论文
作者:
Wang, Ye;Liu, Bixin;Wu, Hongjia;Zhao, Shan;Cai, Zhiping*;...
通讯作者:
Cai, Zhiping;Li, Donghui
作者机构:
[Wang, Ye; Zhao, Shan; Wu, Hongjia; Cai, Zhiping] Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China.
[Liu, Bixin] Acad Mil Med Sci, Beijing 100091, Peoples R China.
[Li, Donghui] Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China.
[Fong, Cheang Chak] Macau Univ Sci & Technol, Fac Informat Technol, Macau 999078, Peoples R China.
通讯机构:
[Cai, Zhiping] N
[Li, Donghui] H
Natl Univ Def Technol, Coll Comp, Changsha 410073, Peoples R China.
Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China.
语种:
英文
关键词:
Activation function;Convolutional neural network;Deceptive reviews;Deep learning;Opinion spam detection
期刊:
计算机、材料和连续体(英文)
ISSN:
1546-2218
年:
2020
卷:
65
期:
1
页码:
355-367
基金类别:
Funding Statement: This work is supported by The National Development Program of China (2018YFB1800202, SQ2019ZD090149, 2018YFB0204301).
机构署名:
本校为通讯机构
摘要:
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...

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