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Language and Ideology in Congress

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  • Diermeier, Daniel
  • Godbout, Jean-François
  • Yu, Bei
  • Kaufmann, Stefan

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.

Suggested Citation

  • Diermeier, Daniel & Godbout, Jean-François & Yu, Bei & Kaufmann, Stefan, 2012. "Language and Ideology in Congress," British Journal of Political Science, Cambridge University Press, vol. 42(1), pages 31-55, January.
    • Handle: RePEc:cup:bjposi:v:42:y:2012:i:01:p:31-55_00
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    Cited by:

    1. Sabina J Sloman & Daniel M Oppenheimer & Simon DeDeo, 2021. "Can we detect conditioned variation in political speech? two kinds of discussion and types of conversation," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-28, February.
    2. Born, Andreas & Janssen, Aljoscha, 2020. "Does a District-Vote Matter for the Behavior of Politicians? A Textual Analysis of Parliamentary Speeches," Working Paper Series 1320, Research Institute of Industrial Economics.
    3. Renáta Németh, 2023. "A scoping review on the use of natural language processing in research on political polarization: trends and research prospects," Journal of Computational Social Science, Springer, vol. 6(1), pages 289-313, April.
    4. Adriana Bunea & Raimondas Ibenskas, 2015. "Quantitative text analysis and the study of EU lobbying and interest groups," European Union Politics, , vol. 16(3), pages 429-455, September.
    5. Richard Hanania, 2021. "The Humanitarian Turn at the UNSC: Explaining the development of international norms through machine learning algorithms," Journal of Peace Research, Peace Research Institute Oslo, vol. 58(4), pages 655-670, July.
    6. Born, Andreas & Janssen, Aljoscha, 2022. "Does a district mandate matter for the behavior of politicians? An analysis of roll-call votes and parliamentary speeches," European Journal of Political Economy, Elsevier, vol. 71(C).
    7. Gavin Abercrombie & Riza Batista-Navarro, 2020. "Sentiment and position-taking analysis of parliamentary debates: a systematic literature review," Journal of Computational Social Science, Springer, vol. 3(1), pages 245-270, April.

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