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Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora

  • Ludovic Rheault (a1) and Christopher Cochrane (a2)

Abstract

Word embeddings, the coefficients from neural network models predicting the use of words in context, have now become inescapable in applications involving natural language processing. Despite a few studies in political science, the potential of this methodology for the analysis of political texts has yet to be fully uncovered. This paper introduces models of word embeddings augmented with political metadata and trained on large-scale parliamentary corpora from Britain, Canada, and the United States. We fit these models with indicator variables of the party affiliation of members of parliament, which we refer to as party embeddings. We illustrate how these embeddings can be used to produce scaling estimates of ideological placement and other quantities of interest for political research. To validate the methodology, we assess our results against indicators from the Comparative Manifestos Project, surveys of experts, and measures based on roll-call votes. Our findings suggest that party embeddings are successful at capturing latent concepts such as ideology, and the approach provides researchers with an integrated framework for studying political language.

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Authors’ note: We thank participants in the annual meeting of the Society for Political Methodology, the Canadian Political Science Association annual conference, the Advanced Computational Linguistics seminar at the University of Toronto, as well as anonymous reviewers for their helpful comments. Replication data is available through the Political Analysis Dataverse (Rheault and Cochrane 2019).

Contributing Editor: Jeff Gill

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Bäck, H., and Debus, M.. 2016. Political Parties, Parliaments and Legislative Speechmaking . New York: Palgrave Macmillan.
Beelen, K., Thijm, T. A., Cochrane, C., Halvemaan, K., Hirst, G., Kimmins, M., Lijbrink, S., Marx, M., Naderi, N., Polyanovsky, R., Rheault, L., and Whyte, T.. 2017. “Digitization of the Canadian Parliamentary Debates.” Canadian Journal of Political Science 50(3):849864.
Benoit, K., and Laver, M.. 2006. Party Policy in Modern Democracies . New York: Routledge.
Bird, K. 2010. “Patterns of Substantive Representation Among Visible Minority MPs: Evidence from Canada’s House of Commons.” In The Political Representation of Immigrants and Minorities , edited by Bird, K., Saalfeld, T., and Wüst, A. M.. New York: Routledge.
Bishop, C. M. 2006. Pattern Recognition and Machine Learning . New York: Springer.
Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., and Tanenbaum, E.. 2001. Mapping Policy Preferences: Estimates for Parties, Electors, and Governments (1945–1998) . Oxford: Oxford University Press.
Budge, I., and Laver, M. J., eds. 1992. Party Policy and Government Coalitions . London: Palgrave Macmillan UK.
Caliskan, A., Bryson, J. J., and Narayanan, A.. 2017. “Semantics Derived Automatically from Language Corpora Contain Human-Like Biases.” Science 356(6334):183186.
Castles, F. G., and Mair, P.. 1984. “Left–Right Political Scales: Some ‘Expert’ Judgments.” European Journal of Political Research 12(1):7388.
Clarke, H. D., Sanders, D., Stewart, M. C., and Whiteley, P.. 2004. Political Choice in Britain . Oxford: Oxford University Press.
Clinton, J. D. 2012. “Using Roll Call Estimates to Test Models of Politics.” Annual Review of Political Science 15:7999.
Cochrane, C. 2010. “Left/Right Ideology and Canadian Politics.” Canadian Journal of Political Science 45(3):583605.
Cochrane, C. 2015. Left and Right: The Small World of Political Ideas . Montreal, Kingston: McGill-Queen’s University Press.
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R.. 1990. “Indexing by Latent Semantic Analysis.” Journal of the American Society for Information Science 41(6):391407.
Denny, M. J., and Spirling, A.. 2018. “Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It.” Political Analysis 26(2):168189.
Diermeier, D., Godbout, J.-F., Yu, B., and Kaufmann, S.. 2012. “Language and Ideology in Congress.” British Journal of Political Science 42(1):3155.
Freeden, M. 1998. Ideology and Political Theory: A Conceptual Approach . Oxford: Oxford University Press.
Gabel, M. J., and Huber, J. D.. 2000. “Putting Parties in Their Place: Inferring Party Left–Right Ideological Positions from Party Manifestos Data.” American Journal of Political Science 44(1):94103.
Garg, N., Schiebinger, L., Jurafsky, D., and Zou, J.. 2018. “Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes.” Proceedings of the National Academy of Sciences 115(16):E3635E3644.
Gentzkow, M., Kelly, B. T., and Taddy, M.. 2017. “Text as Data.” NBER Working Paper w23276.
Gentzkow, M., and Shapiro, J. M.. 2010. “What Drives Media Slant? Evidence from U.S. Daily Newspapers.” Econometrica 78(1):3571.
Gentzkow, M., Shapiro, J. M., and Taddy, M.. 2016. “Measuring Polarization in High-Dimensional Data: Method and Application to Congressional Speech.” NBER Working Paper: 22423.
Glavaš, G., Nanni, F., and Ponzetto, S. P.. 2017. “Cross-Lingual Classification of Topics in Political Texts.” In Proceedings of the 2017 ACL Workshop on Natural Language Processing and Computational Social Science , 4246. Association for Computational Linguistics.
Godbout, J.-F., and Høyland, B.. 2013. “The Emergence of Parties in the Canadian House of Commons (1867–1908).” Canadian Journal of Political Science 46(4):773797.
Grimmer, J., and Stewart, B. M.. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21(3):267297.
Hastie, T., Tibshirani, R., and Friedman, J.. 2009. The Elements of Statistical Learning . Berlin: Springer.
Hirst, G., Riabinin, Y., Graham, J., Boizot-Roche, M., and Morris, C.. 2014. “Text to Ideology or Text to Party Status? In From Text to Political Positions: Text Analysis across Disciplines , edited by Kaal, B., Maks, I., and van Elfrinkhof, A., 93116. Amsterdam: John Benjamins Publishing Company.
Hix, S., and Noury, A.. 2016. “Government–Opposition or Left–Right? The Institutional Determinants of Voting in Legislatures.” Political Science Research and Methods 4(2):249273.
Huber, J., and Inglehart, R.. 1995. “Expert Interpretations of Party Space and Party Locations in 42 Societies.” Party Politics 1(1):73111.
Iyyer, M., Enns, P., Boyd-Graber, J., and Resnik, P.. 2014. “Political Ideology Detection Using Recursive Neural Networks.” In Proceedings of the 2014 Annual Meeting of the Association for Computational Linguistics , 11131122. Association for Computational Linguistics.
Jensen, J., Kaplan, E., Naidu, S., and Wilse-Samson, L.. 2012. “Political Polarization and the Dynamics of Political Language: Evidence from 130 Years of Partisan Speech.” Brookings Papers on Economic Activity Fall:181.
Johnston, R. 2017. The Canadian Party System: An Analytic History . Vancouver: UBC Press.
Kim, I. S., Londregan, J., and Ratkovic, M.. 2018. “Estimating Spatial Preferences from Votes and Text.” Political Analysis 26(2):210229.
Lai, S., Liu, K., Xu, J., and an Zhao, L.. 2016. “How to Generate Good Word Embedding? IEEE Intelligent Systems 31(6):514.
Lauderdale, B. E., and Herzog, A.. 2016. “Measuring Political Positions from Legislative Speech.” Political Analysis 24(3):374394.
Laver, M., Benoit, K., and Garry, J.. 2003. “Extracting Policy Positions from Political Texts Using Words as Data.” American Political Science Review 97(2):311331.
Le, Q., and Mikolov, T.. 2014. “Distributed Representations of Sentences and Documents.” In Proceedings of the 31st International Conference on Machine Learning , edited by Xing, E. P. and Jebara, T., II-1188II-1196. PMLR.
Levy, O., Goldberg, Y., and Dagan, I.. 2015. “Improving Distributional Similarity with Lessons Learned from Word Embeddings.” Transactions of the Association for Computational Linguistics 3:211225.
Lowe, W., and Benoit, K.. 2013. “Validating Estimates of Latent Traits from Textual Data Using Human Judgment as a Benchmark.” Political Analysis 21(3):298313.
MacKay, D. J. C. 1992. “A Practical Bayesian Framework for Backpropagation Networks.” Neural Computation 4(3):448472.
Manning, C. D., Raghavan, P., and Schütze, H.. 2009. An Introduction to Information Retrieval . Cambridge: Cambridge University Press.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J.. 2013. “Distributed Representations of Words and Phrases and their Compositionality.” In Proceedings of the 26th International Conference on Neural Information Processing Systems , 31113119. Neural Information Processing Systems Foundation.
Mikolov, T., Chen, K., Corrado, G., and Dean, J.. 2013. “Efficient Estimation of Word Representations in Vector Space.” In Proceedings of Workshop at ICLR , 112. International Conference on Representation Learning.
Mullainathan, S., and Spiess, J.. 2017. “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives 31(2):87106.
Nay, J. J. 2016. “Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text.” In Proceedings of the 2016 EMNLP Workshop on Natural Language Processing and Computational Social Science , 4954. Association for Computational Linguistics.
Nokken, T. P., and Poole, K. T.. 2004. “Congressional Party Defection in American History.” Legislative Studies Quarterly 29(4):545568.
Pennington, J., Socher, R., and Manning, C. D.. 2014. “Glove: Global Vectors for Word Representation.” In Conference on Empirical Methods in Natural Language Processing (EMNLP) , 15321543. Association for Computational Linguistics.
Poole, K. T., and Rosenthal, H. L.. 2007. Ideology and Congress . New York: Transaction Publishers.
Powell, G. B. 2004. “Political Representation in Comparative Politics.” Annual Review of Political Science 7(1):273296.
Preoţiuc-Pietro, D., Liu, Y., Hopkins, D., and Ungar, L.. 2017. “Beyond Binary Labels: Political Ideology Prediction of Twitter Users.” In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics , 729740. Association for Computational Linguistics.
Proksch, S.-O., and Slapin, J. B.. 2010. “Position Taking in European Parliament Speeches.” British Journal of Political Science 40(3):587611.
Proksch, S.-O., and Slapin, J. B.. 2015. The Politics of Parliamentary Debate . Cambridge: Cambridge University Press.
Proksch, S.-O., Lowe, W., Wäckerle, J., and Soroka, S.. 2018. “Multilingual Sentiment Analysis: A New Approach to Measuring Conflict in Legislative Speeches.” Legislative Studies Quarterly 0(0):135.
Řehůřek, R., and Sojka, P.. 2010. “Software Framework for Topic Modelling with Large Corpora.” In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks , 4550. European Language Resources Association.
Rheault, L., and Cochrane, C.. 2019. “Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora.” https://doi.org/10.7910/DVN/K0OYQF, Harvard Dataverse.
Rheault, L., Beelen, K., Cochrane, C., and Hirst, G.. 2016. “Measuring Emotion in Parliamentary Debates with Automated Textual Analysis.” PLoS ONE 11(12): e0168843.
Schwarz, D., Traber, D., and Benoit, K.. 2017. “Estimating Intra-Party Preferences: Comparing Speeches to Votes.” Political Science Research and Methods 5(2):379396.
Shafer, B. E., and Johnston, R.. 2009. The End of Southern Exceptionalism: Class, Race, and Partisan Change in the Postwar South . Cambridge: Harvard University Press.
Sim, Y., Acree, B. D. L., Gross, J. H., and Smith, N. A.. 2013. “Measuring Ideological Proportions in Political Speeches.” In Proceedings of the 2013 Conference on Empirical Methods of Natural Language Processing (EMNLP) , 91101. Association for Computational Linguistics.
Slapin, J. B., and Proksch, S.-O.. 2008. “A Scaling Model for Estimating Time-Series Party Positions from Texts.” American Journal of Political Science 52(3):705722.
Spirling, A., and McLean, I.. 2007. “UK OC OK? Interpreting Optimal Classification Scores for the UK House of Commons.” Political Analysis 15(1):8596.
Sundquist, J. L. 2011. Dynamics of the Party System . Washington, DC: Brookings Institution Press.
Taddy, M. 2013. “Multinomial Inverse Regression for Text Analysis.” Journal of the American Statistical Association 108(203):755770.
Tran, D., Hoffman, M. D., Saurous, R. A., Brevdo, E., Murphy, K., and Blei, D. M.. 2017. “Deep Probabilistic Programming.” In Proceedings of the 5th International Conference on Learning Representations , 118.
Wittgenstein, L. 2009. Philosophical Investigations . West Sussex, UK: Blackwell.
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