Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora

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Authors Ludovic Rheault, Christopher Cochrane
Journal/Conference Name POLITICAL ANALYSIS
Paper Category
Paper Abstract Word embeddings, the coecients 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. Œis 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 €t these models with indicator variables of the party aliation 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 €ndings 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. †Assistant Professor, Department of Political Science and Munk School of Global A‚airs and Public Policy, University of Toronto. Email: ludovic.rheault@utoronto.ca ‡Associate Professor, Department of Political Science, University of Toronto. Email: christopher.cochrane@utoronto.ca Authors’ Note: We thank participants in the annual meeting of the Society for Political Methdology, 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 available through the Political Analysis Dataverse (Rheault and Cochrane 2019).
Date of publication 2019
Code Programming Language Python
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