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Multi-Label Prediction for Political Text-as-Data

Published online by Cambridge University Press:  14 June 2021

Aaron Erlich
Affiliation:
Department of Political Science, McGill University, Montreal, QC, Canada Centre for the Study of Democratic Citizenship, QC, Canada
Stefano G. Dantas
Affiliation:
Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada
Benjamin E. Bagozzi
Affiliation:
Department of Political Science and International Relations, University of Delaware, Newark, DE, USA. Email: bagozzib@udel.edu
Daniel Berliner
Affiliation:
Department of Government, London School of Economics and Political Science, London, UK
Brian Palmer-Rubin
Affiliation:
Department of Political Science, Marquette University, Milwaukee, WI, USA
Corresponding
E-mail address:

Abstract

Political scientists increasingly use supervised machine learning to code multiple relevant labels from a single set of texts. The current “best practice” of individually applying supervised machine learning to each label ignores information on inter-label association(s), and is likely to under-perform as a result. We introduce multi-label prediction as a solution to this problem. After reviewing the multi-label prediction framework, we apply it to code multiple features of (i) access to information requests made to the Mexican government and (ii) country-year human rights reports. We find that multi-label prediction outperforms standard supervised learning approaches, even in instances where the correlations among one’s multiple labels are low.

Type
Article
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

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Erlich et al. supplementary material

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