基于政民互动大数据的公共服务效能影响因素分析

The Determinants Analysis of Public Service Efficiency Based on G2C Big Data

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.