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Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora

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  • Rheault, Ludovic
  • Cochrane, Christopher

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.

Suggested Citation

  • Rheault, Ludovic & Cochrane, Christopher, 2020. "Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora," Political Analysis, Cambridge University Press, vol. 28(1), pages 112-133, January.
  • Handle: RePEc:cup:polals:v:28:y:2020:i:1:p:112-133_6
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    Cited by:

    1. Miguel Won & Jorge M. Fernandes, 2022. "Analyzing Twitter networks using graph embeddings: an application to the British case," Journal of Computational Social Science, Springer, vol. 5(1), pages 253-263, May.
    2. Luis Felipe Gutiérrez & Neda Tavakoli & Sima Siami-Namini & Akbar Siami Namin, 2022. "Similarity analysis of federal reserve statements using document embeddings: the Great Recession vs. COVID-19," SN Business & Economics, Springer, vol. 2(7), pages 1-28, July.
    3. Latifi, Albina & Naboka-Krell, Viktoriia & Tillmann, Peter & Winker, Peter, 2024. "Fiscal policy in the Bundestag: Textual analysis and macroeconomic effects," European Economic Review, Elsevier, vol. 168(C).
    4. Gloria Gennaro & Elliott Ash, 2022. "Emotion and Reason in Political Language," The Economic Journal, Royal Economic Society, vol. 132(643), pages 1037-1059.
    5. Hugo Subtil & Vincent Verger, 2024. "Rhétorique émotionnelle et montée du populisme au Parlement européen," Institut des Politiques Publiques halshs-04748267, HAL.
    6. Kina, Mehmet Fuat, 2024. "Exploring Recent Ideological Divides in Turkey: Political and Cultural Axes," SocArXiv kp7s2, Center for Open Science.
    7. Anegundi, Aishwarya & Schulz, Konstantin & Rauh, Christian & Rehm, Georg, 2022. "Modelling Cultural and Socio-Economic Dimensions of Political Bias in German Tweets," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 29-40.

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