Political Stance in Danish

Rasmus Lehmann, Leon Derczynski

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Abstrakt

The task of stance detection consists of classifying the opinion within a text towards some target. This paper seeks to generate a dataset of quotes from Danish politicians, label this dataset to allow the task of stance detection to be performed, and present annotation guidelines to allow further expansion of the generated dataset. Furthermore, three models based on an LSTM architecture are designed, implemented and optimized to perform the task of stance detection for the generated dataset. Experiments are performed using conditionality and bi-directionality for these models, and using either singular word embeddings or averaged word embeddings for an entire quote, to determine the optimal model design. The simplest model design, applying neither conditionality or bi-directionality, and averaged word embeddings across quotes, yields the strongest results. Furthermore, it was found that inclusion of the quotes politician, and the party affiliation of the quoted politician, greatly improved performance of the strongest model.
OriginalsprogEngelsk
TitelProceedings of the Nordic Conference of Computational Linguistics (2019)
ForlagLinköping University Electronic Press
Publikationsdato2019
Sider197–207
ISBN (Elektronisk)978-91-7929-995-8
StatusUdgivet - 2019
NavnNEALT (Northern European Association of Language Technology) Proceedings Series
ISSN1736-6305

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