Research on network public opinion prediction based on PSO-BP neural network

In recent years, there has been a substantial surge in the volume of online public opinions, particularly in the context of emergency situations. This surge has accentuated the exigency for enhanced oversight and early warning systems concerning online public sentiment. Given the observed inadequacies in the accuracy of online sentiment prediction, this study utilizes pertinent data derived from microblog trending topics and emergent events. It employs these data to develop a predictive model for online public sentiment through the amalgamation of Particle Swarm Optimization (PSO) and Backpropagation (BP) neural networks. The model is subsequently assessed, and its efficacy is comprehensively scrutinized through illustrative case studies. The findings underscore the notable advantages of the PSO-BP neural network model in the domain of public sentiment prediction, thus furnishing a viable and efficacious tool for anticipatory emergency public sentiment forecasting.