摘要
在纷繁的公共服务与政民互动中,对效果的研究长期受数据采集问题的制约。而随着信息时代的到来,越来越多的政府网站、服务系统成为政府与民众互动的新途径,也记录了大量可供研究的公共服务数据。本研究利用北京市朝阳区政民互动平台的公众反馈大数据,探寻公共服务效能与成本之间的关系。研究引入文本大数据分析领域的潜在狄利克雷模型进行主题建模,通过测度函数表征行政事务成本要素,从而量化探究公共服务效能影响因素。
In the complex public service and interaction between government and citizens, the empirical studies on efficiency were restricted by data collection in the past long time. With the advent of the information era, more and more government websites and service systems have become a new channel for the interaction between government and citizens, while big data of public service also recorded. In this paper, we explore the relationship among the government to citizen ( G2C ) interaction efficiency and cost of the government by using the relevant data of the G2C interaction platform in Chaoyang district, Beijing. The study introduces the latent Dirichlet allocation ( LDA ) in the field of text mining to realize topic modeling, and conduct quantitative research on determinants of the G2C interaction efficiency by constructing functions based on topic probabilities to measure the cost elements of administrative transaction.
作者
马宝君
张楠
谭棋天
Ma Baojun;Zhang Nan;Tan Qitian
出处
《中国行政管理》
CSSCI
北大核心
2018年第10期109-115,共7页
Chinese Public Administration
基金
国家自然科学基金重大研究计划培育项目“基于网络政府数据分析的政策扩散路径与回应关切能力研究”(编号:91646103)
国家自然科学基金面上项目“基于公共衍生大数据分析的政府决策过程重构与评估方法研究”(编号:71473143)
北京市社会科学基金重点项目“政策信息学方法论研究”(编号:15JGA008)
北京市社会科学基金一般项目“北京城市舆情管理衍生大数据的分析与政策评估研究”(编号:17GLB009)
关键词
政民互动
服务效能
成本效益分析
文本挖掘
概率主题建模
G2C interaction
service efficiency
cost-benefit analysis
text mining
probabilistic topic modeling