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Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora

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


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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Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora

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


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