Part of
From Text to Political Positions: Text analysis across disciplinesEdited by Bertie Kaal, Isa Maks and Annemarie van Elfrinkhof
[Discourse Approaches to Politics, Society and Culture 55] 2014
► pp. 93–116
Edited by Bertie Kaal, Isa Maks and Annemarie van Elfrinkhof
[Discourse Approaches to Politics, Society and Culture 55] 2014
► pp. 93–116
Text to Ideology or Text to Party Status?
Recent papers have used support-vector machines with word features to classify political texts by ideology. Our own work on this topic led us to hypothesize that such classifiers are sensitive not to expressions of ideology but rather to expressions of attack and defense, opposition and government. We test this hypothesis by training on one set of parliamentary speeches and testing on another in which party roles have been interchanged, and we find that the performance of the classifier completely disintegrates. Moreover, some features that are indicative of each party ‘swap sides’ with the change of government. Our results suggest that the language of attack and defense, of government and opposition, will dominate and confound any sensitivity to ideology in these kinds of classifiers.
References
DEFT
(2009) Actes de l’atelier de clôture de la cinquième édition du DÉfi Fouille de Textes, Paris 22 June 2009.
Diermeier, D., J.-F. Godbout, B. Yu and S. Kaufmann
2007 Language and ideology in Congress. Annual Meeting of the Midwest Political Science Association.
Forest, D., A. van Hoeydonck, D. Létourneau and M. Bélanger
2009 Impacts de la variation du nombre de traits discriminants sur la catégorisation des documents. Actes de l’atelier de clôture de la cinquième édition du DÉfi Fouille de Textes, Paris, 22 June 2009, pp. 75–88.
Germann, U.
Greene, S.
2007 Spin: Lexical semantics, transitivity, and the identification of implicit sentiment. PhD thesis, University of Maryland, College Park.
Grouin, C., B. Arnulphy, J.-B. Berthelin, S. El Ayari, A. Garcia-Fernandez, A. Grappy, M. Hurault-Plantet, P. Paroubek, I. Robba and P. Zweigenbaum
2009 Présentation de l’édition du D’Efi Fouille de Textes (DEFT’09). Actes de l’atelier de clôture de la cinquième édition du DÉfi Fouille de Textes, Paris, 22 June 2009, pp. 35–50.
Hix, S., A.G. Noury and G. Roland
2007 Democratic Politics in the European Parliament. Cambridge: Cambridge University Press. 
Jiang, M. and S. Argamon
2008 Political leaning categorization by exploring subjectivities in political blogs. Proceedings, 4th International Conference on Data Mining (DMIN 2008), pp. 647–653.
Lin, W., T. Wilson, J. Wiebe and A. Hauptmann
2006 Which side are you on? Identifying perspectives at the document and sentence levels. Proceedings of the 10th Conference on Natural Language Learning (CoNLL-X), pp. 109–116.
Marx, M. and A. Schuth
2010 DutchParl: The parliamentary documents in Dutch. Proceedings of the 7th Conference on Language Resources and Evaluation (LREC 2010), Malta.
Mullen, T. and R. Malouf
2006 A preliminary investigation into sentiment analysis of informal political discourse. Proceedings of the AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pp. 159–162.
Pennebaker, J.W.
Pennebaker, J.W., C.K. Chung, M. Ireland, A. Gonzales and R.J. Booth
Riabinin, Y.
2009 Computational identification of ideology in text: A study of Canadian parliamentary debates. MSc paper, Department of Computer Science, University of Toronto, January 2009 Available at [URL]
Thomas, M., B. Pang and L. Lee
2006 Get out the vote: Determining support or opposition from congressional floor-debate transcripts. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pp. 327–335.
Yu, B., S. Kaufmann and D. Diermeier
2008 Classifying party affiliation from political speech. Journal of Information Technology in Politics 5(1), pp. 33–48.
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2020. Hybrid Content Analysis: Toward a Strategy for the Theory-driven, Computer-assisted Classification of Large Text Corpora. Communication Methods and Measures 14:3 ► pp. 165 ff. 
Baden, Christian, Christian Pipal, Martijn Schoonvelde & Mariken A. C. G van der Velden
2022. Three Gaps in Computational Text Analysis Methods for Social Sciences: A Research Agenda. Communication Methods and Measures 16:1 ► pp. 1 ff.
Cochrane, Christopher, Ludovic Rheault, Jean-François Godbout, Tanya Whyte, Michael W.-C. Wong & Sophie Borwein
2022. The Automatic Analysis of Emotion in Political Speech Based on Transcripts. Political Communication 39:1 ► pp. 98 ff.
Dallmann, Alexander, Florian Lemmerich, Daniel Zoller & Andreas Hotho
2015. Proceedings of the 26th ACM Conference on Hypertext & Social Media - HT '15, ► pp. 133 ff.
Dehghani, Mostafa, Hosein Azarbonyad, Jaap Kamps & Maarten Marx
2016. Two-Way Parsimonious Classification Models for Evolving Hierarchies. In Experimental IR Meets Multilinguality, Multimodality, and Interaction [Lecture Notes in Computer Science, 9822], ► pp. 69 ff.
Rheault, Ludovic & Christopher Cochrane
2020. Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora. Political Analysis 28:1 ► pp. 112 ff.
Zuba, Krzysztof
2021. From the poster boy of Europeanization to the sick man of Europe: thirty years (1990–2019) of Poland’s European policy. European Politics and Society 22:3 ► pp. 394 ff.
This list is based on CrossRef data as of 16 november 2023. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.