Identification-Dissemination-Warning: Algorithm and Prediction of Early Warning of Network Public Opinion

Abstract

In order to better monitor public opinion, this study reviews how the existing thesis work theoretically and practically from the interdisciplinary perspective of communication and computer science and then proposes a new vision under the framework of “identification-dissemination-warning”. Real-life applications include news reports and social media in data collection, emphasizing attitude analysis, building an evaluation system that consisted of four parameters, i.e., event, dissemination, status, and response, determine how serious an emergency is based on how fast public opinion will deteriorate and provide response guidance accordingly. On the theoretical front, this study takes into account the inter-influence between different parameters and optimize semantic analysis, emergency grading and nonlinear processing with the help of Bayesian network, hierarchical network models, grey relational analysis, latent semantic analysis and BP neural network.