Political Stance in Danish

Rasmus Lehmann, Leon Derczynski

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the Nordic Conference of Computational Linguistics (2019)
PublisherLinköping University Electronic Press
Publication date2019
ISBN (Electronic)978-91-7929-995-8
Publication statusPublished - 2019
SeriesNEALT (Northern European Association of Language Technology) Proceedings Series


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