新冠肺炎网络舆情的启示:复工复产的文本挖掘与分析  

Revelation of COVID-19Net-mediated Public Opinion:Text Mining and Analysis of Resumption of Work and Production

作　　者：朱建平[1,2] 王炫力 ZHU Jianping;WANG Xuanli(School of Management,Xiamen University;National Institute for Data Science in Health and Medicine,Xiamen University)

机构地区：[1]厦门大学管理学院,福建厦门361005 [2]厦门大学健康医疗大数据国家研究院,福建厦门361005

出　　处：《数学建模及其应用》2020年第4期37-48,F0003,共13页Mathematical Modeling and Its Applications

基　　金：国家社会科学基金重点项目(20ATJ005)。

摘　　要：统筹推进新冠肺炎疫情防控和复工复产,既是一场大战,也是一场大考.以统筹推进复工复产为主题,采集了2020年2月1日至7日网络舆情数据,经筛选得出11个热点话题,采用有向聚类的方法将11个热点话题分为5类,运用词云、关联规则、复杂网络、文本倾向性分析等数据挖掘与可视化技术,着重描绘了话题的热度差异、内容特征、关联关系以及倾向趋势,从而对这7天内民众对话题的关注特征进行刻画,并给出适当建议.Coordinating the prevention and control of COVID-19 and the resumption of work and production is both a battle and a test.With the theme of promoting the resumption of work and production as a whole,this paper collected net-mediated public opinion data from February 1,2020 solstice to 7.After screening,11 hot topics were divided into 5 categories by directional clustering method.By using data mining and visualization techniques such as word cloud,association rules,complex network,text orientation analysis,etc.,the paper mainly describes the heat difference,content characteristics,relevance relationship and tendency of the topic,so as to describe the characteristics of the public’s attention to the topic in these 7 days and give appropriate suggestions.

关 键 词：统筹推进复工复产 词云 关联规则 复杂网络 文本倾向性分析 

分 类 号：C812[社会学—统计学]