New Media Public Relations Regulation Strategy Model Based on Generative Confrontation Network

Abstract

The rapid development of new media weakens the control of traditional news media on information. At the same time, under the impact of the rapid development of new media, it has brought new challenges to the regulation of public relations. In the new media environment, this paper constructs a text emotion generation model based on GAN to support the application of new media public relations regulation strategy. Aiming at the problem of insufficient constraint information of keywords in text generation, this paper uses the confrontation generation model based on reinforcement learning to supplement sentence components around keywords, so as to generate the text with the highest quality. At the same time, in order to extend GAN from continuous space to discrete space, the differentiable function based on Softmax transformation is adopted to approach the original nondifferentiable function. In this paper, LTP word segmentation system is used to select 356742 pieces of data with a length less than 20 for the experiment. Compared with Seqtoseq+attenion and Transform models, this model has higher similarity of real text distribution and higher text diversity. The retention degree of the main content of the input text is as high as 96.17%, which is higher than that of Seqtoseq+attenion model of 8.49% and Transform model of 6.11%. It provides effective support for the regulation of new media public relations.