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Language and Ideology in Congress

Abstract

Legislative speech records from the 101st to 108th Congresses of the US Senate are analysed to study political ideologies. A widely-used text classification algorithm – Support Vector Machines (SVM) – allows the extraction of terms that are most indicative of conservative and liberal positions in legislative speeches and the prediction of senators’ ideological positions, with a 92 per cent level of accuracy. Feature analysis identifies the terms associated with conservative and liberal ideologies. The results demonstrate that cultural references appear more important than economic references in distinguishing conservative from liberal congressional speeches, calling into question the common economic interpretation of ideological differences in the US Congress.

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Keith T. Poole , Spatial Models of Parliamentary Voting (New York: Cambridge University Press, 2005)

Stephen Purpura and Dustin Hillard , ‘Automated Classification of Congressional Legislation’, Proceedings of the 2006 International Conference on Digital Government Research (2006), 219225

Bjørn Høyland and Jean-François Godbout , ‘Predicting Party Group Affiliation from European Parliament Debates’ (paper presented at the European Consortium for Political Research Meeting of the Standing Group on the European Union (Riga: Latvia, 2008)

Edda Leopold and Jörg Kindermann , ‘Text Categorization with Support Vector Machines: How to Represent Texts in Input Space?’ Machine Learning, 46 (2002), 423444

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British Journal of Political Science
  • ISSN: 0007-1234
  • EISSN: 1469-2112
  • URL: /core/journals/british-journal-of-political-science
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