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Frontiers in Signal Processing
FSP > Volume 4, Number 1, January 2020

Spam Comment Recognition Based on Wide & Deep Learning

Download PDF  (593.6 KB)PP. 30-36,  Pub. Date:October 24, 2019
DOI: 10.22606/fsp.2020.41005

Author(s)
Meiling Fu, Daji Ergu
Affiliation(s)
Key Laboratory of Electronic and Information Engineering,Southwest Minzu University,State Ethnic Affairs Commission, Chengdu, Sichuan 610000, China
Key Laboratory of Electronic and Information Engineering,Southwest Minzu University,State Ethnic Affairs Commission, Chengdu, Sichuan 610000, China
Abstract
The flood of e-commerce platform spam comments affects consumers' purchasing decisions, which greatly damages the interests of consumers. In the process of spam comment recognition, the explicit discrete features of spam comments were usually used as the input of the model. This paper combines the implicit semantic features of spam comments and the explicit discrete features of spam comments to identify the spam comment. First, SMOTE oversampling method is used to balance positive and negative sample sets. Then, wide & deep model, a recommendation system model proposed by Google, is improved and applied to one of the public datasets of spam comment recognition and the commodity datasets collected from one of the biggest e-commerce platform in China. The experimental results show that the improved algorithm can achieve good results in both the gold-standard opinion spam datasets and the commodity datasets.
Keywords
Wide & deep, spam comment, SMOTE, recognition.
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