The Strategy of Discriminating False Comments on the Internet by Fusing Probabilistic Topic and Word Vector Models

Abstract

With the acceleration of the social process of "Internet+", e-commerce has entered an era of rapid development. In response to the subjective judgments in current false information detection methods and the problem of low classification accuracy caused by the failure to extract semantic information hidden in text, this study proposes Latent Dirichlet Allocation -Word vector-Edge Convolutional Neural Network (LW-ECNN) Model. First, the probabilistic topic model is organically combined with the word vector model, and then the edge path structure is proposed to optimize the Convolutional Neural Network (CNN) discriminative classification model. Finally, the data-side optimization module is combined with the edge CNN classifier to form a web-based false comment discrimination model based on the probabilistic topic and word vector models. The results show that the probabilistic topic and word vector-based online false comment discrimination model has good results in detecting false comments, and can provide data reference for false comment detection by false detection-related departments, which is of great practical significance to assist the network environment cleaning work.