Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora. (3rd July 2019)

• Record Type: Journal Article
• Authors: Rheault, Ludovic; Cochrane, Christopher
• Is Part Of: Political analysis. Volume 28:Number 1(2020)
• Page Start: 112
• Page End: 133
• Publication Date: 2019-07-03
• Subjects: word embeddings, -- parliamentary corpora, -- text as data, -- political ideology, -- natural language processing; Political science -- Methodology -- Periodicals; Electronic journals; 320.011
• DOI: 10.1017/pan.2019.26
• Languages: English
• ISSNs: 1047-1987

Abstract

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.