Text and data mining techniques in judgment open data analysis for administrative practice control

Abstract

This paper represents the study results of machine learning methods application for the analysis of judgment open data. The study is dedicated to develop empirical ways to identify the relationships and the structure of administrative law enforcement process based on semi-structured data analysis and give recommendations for improving the administrative regulation. The results of the research can be us ed for legislative, analytical and law enforcement activities in the field of governmental regulation. In the course of data analysis, the models based on decision trees and other machine learning methods is developed. In addition, the models for extracting information from semi-structured texts of court decisions is developed. Moreover, a predictive model of appeal outcome is developed. The effectiveness of the established methods are demonstrated in the recommendation cases for improving the current legislation by the example of administrative law for reducing the burden on public administration.