Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model

Abstract

Public policy and public opinion directly affect the image of the government, but due to the lack of appropriate monitoring and early warning tools, the government’s handling of credit changes is seriously lagging behind. In response to this problem, this paper integrates the internet, public information, market credit information, and other data, uses hidden Markov models and natural language processing technology, and establishes a modern government public policy and public opinion monitoring and early warning model to evaluate government credit in real time; the government can formulate relevant policies based on the evaluation results to improve the government’s governance capabilities. Empirical analysis shows that, based on the dynamic scoring framework and Markov model, the government credit monitoring and early warning models established, respectively, have 90% of the reference value, and the analysis results have the same reference. This method can effectively predict the trend of hot online public opinion. The subsequent establishment of an online public opinion early warning system and an online public opinion guidance mechanism provided theoretical support.