Multi-Label Prediction for Political Text-as-Data. (14th October 2022)

• Record Type: Journal Article
• Authors: Erlich, Aaron; Dantas, Stefano G.; Bagozzi, Benjamin E.; Berliner, Daniel; Palmer-Rubin, Brian
• Is Part Of: Political analysis. Volume 30:Number 4(2022)
• Page Start: 463
• Page End: 480
• Publication Date: 2022-10-14
• Subjects: text-as-data -- multi-label -- machine learning -- classification -- prediction; Political science -- Methodology -- Periodicals; Electronic journals; 320.011
• DOI: 10.1017/pan.2021.15
• Languages: English
• ISSNs: 1047-1987

Abstract

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.