Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach
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Abstract (arXiv)
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 1999-2014. 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 behaviour and the committee structure of the Parliament.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jul 2016.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 3; 2024: 1; 2025: 2.
8 papers.
- 2024Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusioncited by 16
- 2023ANTM: An Aligned Neural Topic Model for Exploring Evolving Topicscited by 6
- 2023What Does the Indian Parliament Discuss? An Exploratory Analysis of the Question Hour in the Lok Sabhacited by 2
- 2021Topic Scaling: A Joint Document Scaling -- Topic Model Approach To Learn Time-Specific Topicscited by 1
- 2021TopicTracker: A Platform for Topic Trajectory Identification and Visualisationcited by 0
- 2023Beyond Sentiment: Leveraging Topic Metrics for Political Stance Classificationcited by 0
- 2025Exploring Topic Trends in COVID-19 Research Literature using Non-Negative Matrix Factorizationcited by 0
- 2025Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Methodcited by 0
- 2025Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method23 Mar 2025
- 2025Exploring Topic Trends in COVID-19 Research Literature using Non-Negative Matrix Factorization23 Mar 2025
- 2024Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion28 May 2024
- 2023Beyond Sentiment: Leveraging Topic Metrics for Political Stance Classification24 Oct 2023
- 2023What Does the Indian Parliament Discuss? An Exploratory Analysis of the Question Hour in the Lok Sabha1 Apr 2023
- 2023ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics3 Feb 2023
- 2021Topic Scaling: A Joint Document Scaling -- Topic Model Approach To Learn Time-Specific Topics31 Mar 2021
- 2021TopicTracker: A Platform for Topic Trajectory Identification and Visualisation2 Mar 2021