Multi-Label Prediction for Political Text-as-Data

Aaron Erlich, Stefano G. Dantas, Benjamin E. Bagozzi, Daniel Berliner and Brian Palmer-Rubin

Political Analysis, 2022, vol. 30, issue 4, 463-480

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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:30:y:2022:i:4:p:463-480_1

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