基于GM(1,1)的网络舆情热度预测模型研究

Researche on the Prediction Model of Network Public Opinion Heat Based on GM(1,1)

对“新冠肺炎”相关的原创微博内容开展耦合GM(1,1)和信息熵的舆情热度预测分析模型研究,实现对公共事件的网络舆情热度的短期预测分析。实验研究结果表明,GM(1,1)模型可以有效地对公开疫情事件的网络舆情热度进行短期预测;另外,从模型分析可得,四月初民众对“新冠肺炎”疫情事件的态度波动很小、基本处于平稳状态,符合现实实际。

This paper studies the prediction and analysis model of public opinion heat coupled with GM(1,1)and information entropy for the original microblog content related to“COVID-19”,and realizes the short-term prediction and analysis of the network public opinion heat of public events.The experimental results show that the GM(1,1)model can effectively predict the online public opinion heat of public epidemic events in the short term.In addition,according to the model analysis,it can be seen that the public s attitude towards the COVID-19 epidemic event fluctuated very little in early April and was basically stable,which is in line with the reality.