Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach

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Title: Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach
Authors: Greene, Derek
Cross, James P.
Permanent link: http://hdl.handle.net/10197/8690
Date: Jan-2017
Abstract: This study analyzes the political agenda of the European Parliament (EP) plenary, how it has evolved over time, and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making plenary speeches. To unveil the plenary agenda and detect latent themes in legislative speeches over time, MEP speech content is analyzed using a new dynamic topic modeling method based on two layers of Non-negative Matrix Factorization (NMF). This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999 to 2014. Our findings suggest that two-layer NMF is a valuable alternative to existing dynamic topic modeling approaches found in the literature, and can unveil niche topics and associated vocabularies not captured by existing methods. Substantively, our findings suggest that the political agenda of the EP evolves significantly over time and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro Crisis. MEP contributions to the plenary agenda are also found to be impacted upon by voting behavior and the committee structure of the Parliament.
Funding Details: Irish Research Council
Science Foundation Ireland
Type of material: Journal Article
Publisher: Cambridge University Press
Copyright (published version): 2017 the Authors
Keywords: Machine learning;Statistics
DOI: 10.1017/pan.2016.7
Language: en
Status of Item: Peer reviewed
Appears in Collections:Politics and International Relations Research Collection
Computer Science Research Collection
Insight Research Collection

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