Political Stance Detection for Danish

Rasmus Lehmann and Leon Derczynski

NODALIDA 2019

The task of stance detection consists of classifying the opinion expressed within a text towards some target. This paper presents a dataset of quotes from Danish politicians, labelled for stance, and also stance detection results in this context. Two deep learning-based models are designed, implemented and optimized for political stance detection. The simplest model design, applying no conditionality, and word embeddings averaged across quotes, yields the strongest results. Furthermore, it was found that inclusion of the quote’s utterer and the party affiliation of the quoted politician, greatly improved performance of the strongest model.

Dansk abstrakt: I indeværende artikel præsenteres et annoteret datasæt over citater fra danske politikere, samt to Deep Learning-baserede modeller til brug ved identifikation af holdninger i de annoterede citater. Det konkluderes at den simpleste af de to modeller opnår de bedste resultater, samt at brug af information vedrørende citaternes kontekst forbedrer modellernes resultater.

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