基于变分模态分解和IGJO-SVR的网络舆情预测

Network Public Opinion Prediction Based on Variational Mode Decomposition and IGJO-SVR

网络舆情演化趋势预测在当今的网络环境中对相关政府部门监管舆情发展和维持社会舆论稳定具有十分重要的现实意义。本文针对网络舆情数据的特殊性以及考虑模型预测结果的精确性,使用变分模态分解(VMD)和改进后的金豺优化支持向量回归(IGJO-SVR)构建网络舆情演化趋势预测模型,并以“北溪”事件相关舆情数据为案例进行实证研究,对比结果表明,本文所构建的预测模型精度显著优于其余模型。基于变分模态分解VMD和IGJO-SVR的网络舆情热度预测模型具有较为优秀的预测精度,在实际工作中可为相关政府部门提供切实有效的舆情态势研判和决策帮助。

The prediction of the evolution trend of network public opinion has very important practical significance for the relevant government departments to supervise the development of public opinion and maintain the stability of public opinion in today′s network environment.Aiming at the particularity of network public opinion data and considering the accuracy of model prediction results,this paper uses variational mode decomposition(VMD)and improved golden jackal optimization support vector regression(IGJO-SVR)to construct a network public opinion evolution trend prediction model,and takes‘Beixi’event-related public opinion data as a case for empirical research.The comparison results show that the accuracy of the prediction model constructed in this paper is significantly better than the other models.The network public opinion heat prediction model based on variational mode decomposition VMD and IGJO-SVR has excellent prediction accuracy,and can provide effective public opinion situation analysis and decision-making help for relevant government departments in practical work.