Spam Comment Recognition Based on Wide & Deep Learning

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.