Abstract
PurposeIn the new era of highly developed Internet information, the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.Design/methodology/approachAiming at the complex and nonlinear characteristics of the network public opinion, considering the accuracy and stability of the applicable model, a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network (BES-RBF) is proposed. Empirical research is conducted with Baidu indexes such as “COVID-19”, “Winter Olympic Games”, “The 100th Anniversary of the Founding of the Party” and “Aerospace” as samples of network public opinion.FindingsThe experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information, has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.Originality/valueA method for optimizing the central value, weight, width and other parameters of the radial basis function neural network with the bald eagle algorithm is given, and it is applied to network public opinion trend prediction. The example verifies that the prediction algorithm has higher accuracy and better stability.
Reference29 articles.
1. Novel meta-heuristic bald eagle search optimisation algorithm;Artificial Intelligence Review,2020
2. Hybrid Grey Wolf: bald Eagle search optimized support vector regression for traffic flow forecasting;Journal of Ambient Intelligence and Humanized Computing,2020
3. Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks;International Journal of Intelligent Computing and Cybernetics,2020
4. Research on the prediction of network public opinion based on SAPSO_RBF neural network;Journal of Wuhan University of Technology (Information and Management Engineering),2017
5. Particle swarm optimization algorithm;Information and Control,2005
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献