融合微博热点分析和LSTM模型的网络舆情预测方法

Network public opinion forecasting method fusing microblog hotspot analysis and LSTM model

当前互联网已成为公众获取信息、表达观点的重要平台,也带来社会舆情事件易发生的风险,通过对网络舆情走势的提前预测,能够准确判断热点事件的发展态势,为政府相关部门应对舆情危机提供参考.针对单一预测模型预测精度不高和社交媒体对舆情走势影响较大的问题,提出了融合微博热点分析和长短期记忆神经网络(LSTM)的舆情预测方法.利用网络爬虫和PyTorch机器学习平台构建了用于舆情时序数据分析的网络舆情预测系统;在此系统内,考虑微博的强时效性,采用网络热点分析技术计算微博热度分值;改进LSTM网络,设计由2个隐含层组成的MH-LSTM预测模型;将MH-LSTM模型用于舆情事件百度指数的定量预测中,通过试验验证了模型的正确性,证实了该预测模型拥有较好的预测效果.

Nowadays,the Internet not only becomes an important platform for the public to obtain information and express views,but also brings the risk of social public opinion events.By predicting the trend of network public opinion in advance,the development of hot events can be accurately judged to provide suggestions to relevant departments of government for dealing with public opinion crisis.To solve the problems of poor prediction of single prediction model and great influence of social media on the trend of public opinion,a public opinion prediction method was proposed based on microblog hotspot analysis and LSTM neural network.The network public opinion prediction system was constructed for public opinion time series data analysis by web crawler and PyTorch machine learning platform.Considering the strong currency of microblog,the microblog heat score was calculated with the network hotspot analysis technology.LSTM network was improved,and MH-LSTM prediction model with two hidden layers was designed.Applying MH-LSTM model into the quantitative prediction of Baidu index of public opinion events,the experiments show the correctness with good prediction effect of the proposed model.