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

Published online by Cambridge University Press:  13 March 2017

Derek Greene  and
James P. Cross
Derek Greene
Affiliation:
Insight Centre for Data Analytics & School of Computer Science, University College Dublin, Ireland. Email: [email protected]
James P. Cross*
Affiliation:
School of Politics & International Relations, University College Dublin, Ireland. Email: [email protected]
*
*Email: [email protected]
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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.

Type
Articles
Information
Political Analysis , Volume 25 , Issue 1 , January 2017 , pp. 77 - 94
Copyright
Copyright © The Author(s) 2017. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Authors’ note: The authors would like to thank Prof. David Farrell, Dr. Jos Elkink, and the panel participants at the 2015 EPSA General Conference, the 2015 ACM Web Science Conference, and workshops in UCD, Dublin and the EUENGAGE Automated Text Analysis Workshop in Amsterdam for their helpful comments. We would also like to thank the editor and the three anonymous reviewers who provided constructive feedback that significantly improved the final paper. For Dataverse replication materials, see Cross and Greene (2016).This research was partly supported by the Irish Research Council (IRC) New Foundations Scheme and the Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.

Contributing Editor: R. Michael Alvarez

References

Alexandrova, P., Carammia, M., and Timmermans, A.. 2012. Policy punctuations and issue diversity on the european council agenda. Policy Studies Journal 40(1):69–88.Google Scholar
Baumgartner, F. R., Breunig, C., Green-Pedersen, C., Jones, B. D., Mortensen, P. B., Nuytemans, M., and Walgrave, S.. 2009. Punctuated equilibrium in comparative perspective. American Journal of Political Science 53(3):603–620.Google Scholar
Baumgartner, F. R., and Jones, B. D.. 2010. Agendas and instability in American politics . Chicago: University of Chicago Press.Google Scholar
Blei, D. M., and Lafferty, J. D.. 2006. Dynamic topic models. In Proc. 23rd International Conference on Machine Learning , ACM, pp. 113–120.Google Scholar
Blei, D. M., Ng, A. Y., and Jordan, M. I.. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3:993–1022.Google Scholar
Boutsidis, C., and Gallopoulos, E.. 2008. SVD based initialization: A head start for non-negative matrix factorization. Pattern Recognition 41(4):1350–1362.Google Scholar
Bowler, S., and Farrell, D. M.. 1995. The organizing of the european parliament: Committees, specialization and co-ordination. British J. Political Science 25(02):219–243.Google Scholar
Cameron, A. C., and Trivedi, P. K.. 2013. Regression analysis of count data, Vol. 53 . Cambridge: Cambridge University Press.Google Scholar
Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., and Blei, D. M.. 2009. Reading tea leaves: How humans interpret topic models. In Advances in neural information processing systems , pp. 288–296.Google Scholar
Cross, J. P., and Greene, D.. 2016. “Replication data for: Exploring the political agenda of the european parliament using a dynamic topic modeling approach”. doi:10.7910/DVN/LVHLZK.Google Scholar
Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., and Harshman, R. A.. 1990. Indexing by latent semantic analysis. Journal of the American Society of Information Science 41(6):391–407.Google Scholar
Dowding, K., Hindmoor, A., and Martin, A.. 2016. The Comparative Policy Agendas Project: theory, measurement and findings. Journal of Public Policy 36(01):3–25.Google Scholar
Downs, A. 1996. Up and Down with Ecology: The “Issue-Attention Cycle”. In The Politics of American Economic Policy Making , ed. Peretz, P.. Armonk, NY: M.E. Sharpe.Google Scholar
Grimmer, J. 2010. A bayesian hierarchical topic model for political texts: Measuring expressed agendas in senate press releases. Political Analysis 18(1):1–35.Google Scholar
Hix, S., Noury, A., and Roland, G.. 2006. Dimensions of politics in the European parliament. American J. Political Science 50(2):494–520.Google Scholar
Hix, S., Noury, A., and Roland, G.. 2007. Democratic politics in the European Parliament . Cambridge: Cambridge University Press.Google Scholar
Jennings, W., Bevan, S., Timmermans, A., Breeman, G., Brouard, S., Chaqués-Bonafont, L., Green-Pedersen, C., John, P., Mortensen, P. B., and Palau, A. M.. 2011. Effects of the core functions of government on the diversity of executive agendas. Comparative Political Studies , 0010414011405165.Google Scholar
John, P., and Bevan, S.. 2012. What are policy punctuations? large changes in the legislative agenda of the uk government, 1911–2008. Policy Studies Journal 40(1):89–108.Google Scholar
Jones, B. D. 1994. Reconceiving decision-making in democratic politics: attention, choice, and public policy . Chicago: University of Chicago Press.Google Scholar
Jones, B. D., and Baumgartner, F. R.. 2005. The politics of attention: How government prioritizes problems . University of Chicago Press.Google Scholar
Jones, B. D., and Baumgartner, F. R.. 2012. From there to here: Punctuated equilibrium to the general punctuation thesis to a theory of government information processing. Policy Studies Journal 40(1):1–20.Google Scholar
Lee, D. D., and Seung, H. S.. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401:788–791.Google Scholar
McCallum, A. K.2002. MALLET: A machine learning for language toolkit, http://mallet.cs.umass.edu.Google Scholar
Mikolov, T., Chen, K., Corrado, G., and Dean, J.. 2013. Efficient estimation of word representations in vector space. CoRR arXiv:abs/1301.3781.Google Scholar
O’Callaghan, D., Greene, D., Carthy, J., and Cunningham, P.. 2015. An analysis of the coherence of descriptors in topic modeling. Expert Systems with Applications (ESWA) 42(13):5645–5657.Google Scholar
Proksch, S.-O., and Slapin, J. B.. 2010. Position taking in European Parliament speeches. British J. Political Science 40(03):587–611.Google Scholar
Proksch, S.-O., and Slapin, J. B.. 2014. The politics of parliamentary debate: Parties, rebels and representation . Cambridge: Cambridge University Press.Google Scholar
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., and Radev, D. R.. 2010. How to analyze political attention with minimal assumptions and costs. American J. Political Science 54(1):209–228.Google Scholar
Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B., and Rand, D. G.. 2014. Structural topic models for open-ended survey responses. American J. Political Science 58(4):1064–1082.Google Scholar
Röder, M., Both, A., and Hinneburg, A.. 2015. Exploring the space of topic coherence measures. In Proc. 8th ACM international conference on Web search and data mining ACM, pp. 399–408.Google Scholar
Scully, R., Hix, S., and Farrell, D. M.. 2012. National or European Parliamentarians? Evidence from a New Survey of the Members of the European Parliament. JCMS: J. Common Market Studies 50(4):670–683.Google Scholar
Slapin, J. B., and Proksch, S. O.. 2010. Look who’s talking: Parliamentary debate in the European Union. European Union Politics 11(3):333–357.Google Scholar
Stevens, K., Kegelmeyer, P., Andrzejewski, D., and Buttler, D.. 2012. Exploring topic coherence over many models and many topics. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning Association for Computational Linguistics, pp. 952–961.Google Scholar
Steyvers, M., and Griffiths, T.. 2006. Probabilistic Topic Models. In Handbook of Latent Semantic Analysis , ed. Landauer, T., Mcnamara, D., Dennis, S., and Kintsch, W.. Mahwah, NJ: Laurence Erlbaum.Google Scholar
Sulo, R., Berger-Wolf, T., and Grossman, R.. 2010. Meaningful selection of temporal resolution for dynamic networks. In Proc. 8th Workshop on Mining and Learning with Graphs ACM, pp. 127–136.Google Scholar
Wang, Q., Cao, Z., Xu, J., and Li, H.. 2012. Group matrix factorization for scalable topic modeling. In Proc. 35th SIGIR Conf. on Research and Development in Information Retrieval ACM, pp. 375–384.Google Scholar
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