关键词: 虚假评论, 生成式对抗网络, 多层感知机, 支持向量机, 机器学习

Abstract: The development of machine learning related technology has improved the accuracy of onlinedeceptiveyeview identification. However, the current machine learning model lacks enough labeled data to carry out model training. This paper proposes a review dataset expansion method called GAN-RDE based on Generative Adversarial Networks (GAN), which aims to solve the problem of insufficient model training data in deceptive review identification. Specifically, we divide the initial review data into a real review dataset and a deceptive review dataset, and the GAN is trained through the truthful review dataset and the deceptive review dataset respectively, to generate a vector that conforms to the feature distribution of the truthful review and the deceptive review. Secondly, we combine the vector of the review feature distribution with the feature word vector matrix of the initial review dataset to expand the model training data. Finally, the Nai ve Bayes, the multi-layer perceptron, and support vector machine are used as basic classifiers to compare the effects of deceptive review recognition before and after data expansion. The experimental results show that the classifier with the GAN-RDE method can produce better performances than the classifier with the unexpanded dataset in deceptive review identification.

Key words: deceptive review, generative adversarial networks, multi-layer perceptron, support vector machine, machine learning