电子政务环境下行为智力分析的国际经验与方法

I. Alakbarova, Kamala Salayeva
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引用次数: 0

摘要

互联网在人们日常生活中的作用、社交网络对舆论形成的影响、移动通信的普及、电子政务环境下电子信息系统中个人信息的收集,使得“行为分析”问题变得更加相关。在信息社会形成的过程中,为了提高公共行政过程的效率,政府组织要履行的最重要的任务之一就是对公民的行为进行正确的评估和预测,做出正确的决策。行为的智力分析的主要目标是理解个人和社会群体活动的逻辑。本文研究了行为智力分析的国际实践,考察了该领域使用的方法和算法,并指出了存在的问题。为有效解决电子政务环境中行为的智力分析问题,提出了建议。我们提出的基于文本信息的智能行为分析方法包括4个层次:1)初级处理,2)文档描述,3)将一组文档分为正面和负面类,4)确定分类的准确性和完整性特征。使用语义指标对行为进行智力分析,有助于更准确地开展研究,有效地解决行为预测问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
International experience and approaches to the intellectual analysis of behavior in the e-government environment
The role of the Internet in people’s daily lives, the impact of social networks on the formation of public opinion, the spread of mobile communications, the collection of personal information in electronic information systems in the e-government environment made the problem of “behavior analysis” even more relevant. In order to improve the efficiency of the public administration process during the formation of the information society, one of the most important tasks to be performed by the government organizations is the correct assessment and prediction of citizens’ behavior and making the right decisions. The main goal of the intellectual analysis of behavior is to understand the logic of the activities of individuals and social groups. This article studies the international practice in intellectual analysis of behavior, examines the methods and algorithms used in this area, and identifies problems. Proposals are developed for the effective solution of questions on the intellectual analysis of behavior in the e-government environment. The approach we propose for intellectual analysis of behavior based on textual information consists of 4 levels: 1) primary processing, 2) document description, 3) classification of a set of documents into positive and negative classes, 4) determination of accuracy and completeness characteristics in classification. The use of semantic indicators for intellectual analysis of behavior can help conduct research with greater accuracy and effectively solve behavioral prediction problems.
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