Overview of Dynamic Topic Modeling in the European Parliament
The paper "Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach" presents a sophisticated analysis of the evolving political agenda within the European Parliament (EP) plenary sessions. The authors employ a two-layer Non-negative Matrix Factorization (NMF) method to perform dynamic topic modeling on a dataset comprising all English-language legislative speeches from 1999 to 2014. This approach enables the identification of latent themes and topic evolution in MEP speeches, showcasing the adaptability of parliamentary discourse in response to both internal dynamics and external events.
Methodological Contributions
The primary methodological contribution of this paper is the development and application of a dynamic topic modeling strategy utilizing NMF. This approach is notable for its capacity to detect nuanced topics and specialized vocabularies that existing models, such as LDA, may miss. The NMF method is applied in two layers: the first layer generates window topic models from sequential time slices, while the second layer combines these to produce dynamic topics spanning multiple time periods.
The process involves transforming speech data into a document-term matrix followed by NMF decomposition to discern topics. A key aspect is the use of TC-W2V coherence measures for parameter selection, ensuring the semantic validity of detected topics. This method excels in pinpointing specific undercurrents in political speeches, substantiating its advantage in analyzing technocratic discussions frequently encountered in EU politics.
Substantive Insights
Analyzing roughly 210,247 speeches, the paper reveals significant variance in the EP political agenda over the examined period. The researchers highlight how the plenary sessions reflect broader EU policy concerns as well as reactive adjustments to external stimuli, such as referenda outcomes and economic events.
The paper details the dynamic response of EP agenda to exogenous shocks such as the Euro-crisis and Treaty referenda, demonstrating the punctuated equilibrium theory of political attention. These instances delineate an EP agenda that fluctuates in response to pressing issues, thus exposing underlying drivers of MEP speech-making behaviors influenced by voting patterns and committee roles.
Implications and Speculation
The findings have practical implications for understanding the mechanisms of political discourse in EP sessions and can inform future enhancements in tracking political attention. The NMF results suggest that EP agendas encapsulate both broad procedural topics and specific policy debates, a duality that can guide more targeted political science inquiries.
Speculatively, advancements in AI methods like NMF highlight the potential for integrating richer metadata and external datasets in dynamic linguistic analysis. The approach could be expanded to encompass other multilingual or multi-institutional datasets, providing an even more comprehensive depiction of political agendas.
Future Directions
The paper suggests avenues for future research into connecting detected political attention patterns with legislative outcomes, thereby elucidating the influence MEPs exert over policy decisions. Moreover, applying this two-layer NMF approach beyond the European context, including in non-parliamentary domains such as media analysis, could offer fresh perspectives on agenda-setting.
Overall, the paper contributes a nuanced understanding of parliamentary discourse and advances methodological approaches to political text analysis, promising further developments in computational political science.