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Multi-Label Prediction for Political Text-as-Data

Published online by Cambridge University Press:  14 June 2021

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


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.

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

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Edited by Jeff Gill


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