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

作　　者：刘定一[1] 沈阳阳 詹天明 刘亚军[3] 应毅[1] LIU Dingyi;SHEN Yangyang;ZHAN Tianming;LIU Yajun;YING Yi(College of Computer Science and Technology,Sanjiang University,Nanjing,Jiangsu 210012,China;School of Information Engineering,Nanjing Audit University,Nanjing,Jiangsu 211815,China;School of Computer Science and Engineering,Southeast University,Nanjing,Jiangsu 210096,China)

机构地区：[1]三江学院计算机科学与工程学院,江苏南京210012 [2]南京审计大学信息工程学院,江苏南京211815 [3]东南大学计算机科学与工程学院,江苏南京210096

出　　处：《江苏大学学报（自然科学版）》2021年第5期546-553,共8页Journal of Jiangsu University：Natural Science Edition

基　　金：江苏省高等学校自然科学研究面上项目(18KJB520042);国家自然科学基金资助项目(61976117);江苏省自然科学基金资助项目(BK20191409);江苏省高等学校自然科学研究重大项目(19KJA360001);三江学院校级科研重大项目(2019SJKY006)。

摘　　要：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.

关 键 词：网络舆情预测 神经网络 LSTM 微博热点分析 百度指数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]