Empirical Research on E-Government Based on Content Mining
According to acquiring data from the meta-search engine and getting information in specific websites, the author proposes an extraction model based on Web information which is used to construct network relationships of the subject based on its semantic link. Then based on the proposed model above, the author does content mining and semantic analysis on the Web data of five big cities (Beijing, Shanghai, Wuhan, Guangzhou, and Chengdu) with the help of self-made ROST Content Mining System, to get first 30 high-frequency e-government words respectively, and takes Shanghai for specific analysis; Meanwhile, the author, using ROST WebSpider to collect the web page from level 1 to 3 of governments websites in Beijing, Shanghai, Wuhan, Guangzhou and Chengdu, constructs the evaluation model SCISS to do comparative analysis on the development of the five metropolis e-government. Finally, the author comes up with some countermeasures, aiming to provide advice for the development of e-government in china, according to the empirical analysis.
social network E-governmen meta-search engine content mining
Yang SHEN Zitao LIU Shaoji LUO Huijuan FU Ye Li
School of Information Management Wuhan University Wuhan, China International School of Software Wuhan University School of Economics and Management Wuhan University School of Electronic Information Wuhan University
国际会议
南昌
英文
91-94
2009-09-01（万方平台首次上网日期，不代表论文的发表时间）