| 基于BP神经网络的突发传染病舆情热度趋势预测模型研究 |
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| 引用本文： | 曾子明,黄城莺.基于BP神经网络的突发传染病舆情热度趋势预测模型研究[J].现代情报,2018,38(5):37. |
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| 作者姓名： | 曾子明 黄城莺 |
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| 作者单位： | 1. 武汉大学信息资源研究中心, 湖北 武汉 430072;2. 武汉大学图书情报实验教学中心, 湖北 武汉 430072 |
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| 基金项目： | 教育部人文社会科学重点研究基地重大项目"大数据资源的智能化管理与跨部门交互研究——面向公共安全领域"（项目编号：16JJD870003）。 |
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| 摘 要： | ［目的/意义］研究突发传染病舆情热度的发展趋势，能够为制定舆情引导策略提供参考，具有重要的理论意义。［方法/过程］本文首先构建微博舆情热度评价指标体系，基于信息熵确定各个指标的权重，然后对求得的舆情热度趋势值进行分类，在此基础上，建立基于BP神经网络的突发传染病舆情热度趋势预测模型。以新浪微博为例，选取"MERS病毒卫生突发事件"的舆情热度数据进行实例分析，预测该突发传染病事件的发展趋势，从而验证模型的可行性。［结果/结论］实验结果表明，该模型能有效预测突发传染病舆情热度趋势，进而为舆情管控提供决策支持。
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| 关 键 词： | BP神经网络 舆情热度 突发传染病 微博 预测模型 |
Research on Public Opinion Heat Trend Prediction Model of Emergent Infectious Diseases Based on BP Neural Network |
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| Authors: | Zeng Ziming Huang Chengying |
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| Institution: | 1. Center of Information Resources Research, Wuhan University, Wuhan 430072, China;2. Library and Information Experimental Teaching Center, Wuhan University, Wuhan 430072, China |
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| Abstract: | Purpose/Significance] It is of great theoretical significance to study the development trend of public opinion in emergent infectious diseases,which can provide reference for making public opinion guidance strategy.Method/Process] The paper first constructed the index system of microblog public opinion and evaluated the weight of each index based on the information entropy,and then classified the obtained public opinion heat trend,on the base of which,it established the public opinion heat trend prediction model of emergent infectious diseases bases on BP neural network.Taking Sina microblog as an example,it analyzed the public opinion heat data of "MERS virus" to predict the development trend of the emergent infectious disease event,and verified the feasibility of the model.Result/Conclusion] The experimental results showed that the model could effectively predict the trend of public opinion in emergent infectious diseases,and then provide decision support for public opinion control. |
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| Keywords: | BP neural network heat of public opinion emergent infectious diseases microblog prediction model |
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