Abstract
This paper dissects the potential of state-of-the-art computational analysis to promote the investigation of government’s administrative decisions and politics. The Executive Branch generates massive amounts of textual data comprising daily decisions in several levels and stages of the law and decree-making processes. The use of automated text analysis to explore this data based on the substantive interests of scholars runs into computational challenges. Computational methods have been applied to texts from the Legislative and Judicial Branches; however, there barely are suitable taxonomies to automate the classification and analysis of the Executive’s administrative decrees. To solve this problem, we put forward a computational framework to analyze the Brazilian administrative decrees from 2000 to 2019. Our strategy to uncover the contents and patterns of the presidential decree-making is developed in three main steps. First, we conduct an unsupervised text analysis through the LDA algorithm for topic modeling. Second, building upon the LDA results, we propose two taxonomies for the classification of decrees: (a) the ministerial coauthorship of the decrees to map policy areas and (b) the decrees’ fields of law based on a tagging system provided by the Brazilian Senate. Using these taxonomies, we compare the performance of three supervised text classification algorithms: SVM, Convolutional Neural Network, and Hierarchical Attention Network, achieving F1-scores of up to 80% when automatically classifying decrees. Third, we analyze the network generated by links between decrees through centrality and clustering approaches, distinguishing a set of administrative decisions related to the president’s priorities in the economic policy area. Our findings confirm the potential of our computational framework to explore N-large datasets, advance exploratory studies, and generate testable propositions in different research areas. They advance the monitoring of Brazil’s administrative decree-making process that is shaped by the president’s priorities and by the interplay among cabinet members.
Publisher
Public Library of Science (PLoS)
Reference42 articles.
1. Joachims T. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Proceedings of the 10th European Conference on Machine Learning. ECML’98. Berlin, Heidelberg: Springer-Verlag; 1998. p. 137–142. Available from: https://doi.org/10.1007/BFb0026683.
2. Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies; 2016. p. 1480–1489.
3. Luz de Araujo PH, de Campos TE, Ataides Braz F, Correia da Silva N. VICTOR: a Dataset for Brazilian Legal Documents Classification. In: Proceedings of the 12th Language Resources and Evaluation Conference. Marseille, France: European Language Resources Association; 2020. p. 1449–1458. Available from: https://www.aclweb.org/anthology/2020.lrec-1.181.
4. Network analysis and political science;MD Ward;Annual Review of Political Science,2011
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献