Analysis and Management of Flu Disease Public Opinion Based on Machine Learning

Abstract

In the new media era, there are more ways of information dissemination, and the speed of information dissemination becomes faster. Along with it, various public opinions and rumors flood the cyberspace. As a mainstream social media information publishing platform, microblog has become the main way for netizens to obtain, disseminate and publish information. Because microblog can freely make speeches, and has a fast transmission speed and a wide range, it is easy for public opinion information to be widely disseminated in a short time. In particular, information such as rumors in public opinion can affect the network environment and social stability. Therefore, it is necessary to analyze and predict public opinion changes and to provide early warning. The literature uses the classic BP-NN (BP-NN) as the base prediction model, and uses the information published on the Sina microblog platform as a sample to analyze and predict the public opinion of influenza diseases. Due to the BP-NN’ slow convergence speed, this paper introduces an improved genetic algorithm to select the optimal parameters in the BP-NN (IGA-BP-NN), shorten the calculation time, and improve the analysis and prediction efficiency. The experiments verify that the work in this paper can provide more accurate early-warning information for the public opinion management of related departments.