Automatic Thematic Classification of the Titles of the Seimas Votes

Statistical analysis of parliamentary roll call votes is an important topic in political science as it reveals ideological positions of members of parliament and factions. However, these positions depend on the issues debated and voted upon as well as on attitude towards the governing coalition. Therefore, analysis of carefully selected sets of roll call votes provides deeper knowledge about members of parliament behavior. However, in order to classify roll call votes according to their topic automatic text classifiers have to be employed, as these votes are counted in thousands. In this paper we present results of an ongoing research on thematic classification of roll call votes of the Lithuanian Parliament. Also, this paper is a part of a larger project aiming to develop the infrastructure designed for monitoring and analyzing roll call voting in the Lithuanian Parliament.

[1]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[2]  Tomas Krilavicius,et al.  "Mining Social Science Data: a Study of Voting of the Members of the Seimas of Lithuania by Using Multidimensional Scaling and Homegeneity Analysis" , 2011 .

[3]  Andreas Hotho,et al.  A Brief Survey of Text Mining , 2005, LDV Forum.

[4]  Michael A. Bailey Comparable Preference Estimates across Time and Institutions for the Court, Congress, and Presidency , 2007 .

[5]  Steven S. Smith,et al.  The Dimensionality of Congressional Voting Reconsidered , 2016 .

[6]  Jurgita Kapociute-Dzikiene,et al.  Predicting Party Group from the Lithuanian Parliamentary Speeches , 2014, Inf. Technol. Control..

[7]  Gérard Roland,et al.  Dimensions of politics in the European Parliament , 2006 .

[8]  D. S. Guru,et al.  Representation and Classification of Text Documents: A Brief Review , 2010 .

[9]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[10]  Anthony J. Madonna,et al.  Viva Voce: Implications from the Disappearing Voice Vote, 1865–1996 , 2008 .

[11]  Yvonne Herz,et al.  Spatial Models Of Parliamentary Voting , 2016 .

[12]  Vytautas Mickevicius,et al.  Analysing voting behavior of the Lithuanian parliament using cluster analysis and multidimensional scaling : technical aspects , 2014 .

[13]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[14]  Walter Daelemans,et al.  Improving Topic Classification for Highly Inflective Languages , 2012, International Conference on Computational Linguistics.

[15]  Aleks Jakulin,et al.  Analyzing the U.S. Senate in 2003: Similarities, Clusters, and Blocs , 2009, Political Analysis.

[16]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.