[Objective] This paper tries to predict the development trend of online public opinion in emergencies. [Methods] First, we identified multiple uncertain factors affecting the evolution of online public opinion. Then, we constructed a CEEMDAN-BP prediction model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, phase-space reconstruction and Back Propagation Network. Finally, we conducted an empirical study to examine the new model with three emergencies. [Results] Our CEEMDAN-BP model could better predict the development trend of online public opinion in emergencies. The average absolute errors of prediction in three emergencies were 8.60%, 17.98% and 11.97%, respectively. Our model’s prediction accuracy and stability were better than the existing ones. [Limitations] The experimental data was based on the daily statistics, which could not fully reflect the changing public opinion. [Conclusions] The CEEMDAN-BP model can effectively predict the development trend of online public opinion in emergencies, which helps related departments to prepare for and manage the emergencies.
程铁军, 王曼, 黄宝凤, 冯兰萍. 基于CEEMDAN-BP模型的突发事件网络舆情预测研究*[J]. 数据分析与知识发现, 2021, 5(11): 59-67.
Cheng Tiejun, Wang Man, Huang Baofeng, Feng Lanping. Predicting Online Public Opinion in Emergencies Based on CEEMDAN-BP. Data Analysis and Knowledge Discovery, 2021, 5(11): 59-67.
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