Abstract
We analyze the use of language models for political text classification. Political texts become increasingly available and language models have succeeded in various natural language processing tasks. We apply two baselines and different language models to data from the UK, Germany, and Norway. Observed accuracy shows language models improving on the performance of the baselines by up to 10.35% (Norwegian), 12.95% (German), and 6.39% (English).
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Notes
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The Treaty of the European Union states that “Every citizen shall have the right to participate in the democratic life of the Union. Decisions shall be taken as openly and as closely as possible to the citizen.” (see http://data.europa.eu/eli/treaty/teu_2016/art_10/oj).
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We use the MultinomialNB classifier, remove stopwords (Norwegian/German/English), use n-grams from 1 to 4. We determine the best hyperparameter configuration with grid search over maximum number of features \( \{30k, 50k, 100k\} \) and the learning rate \( \alpha \in \{0.01, 0.1, 0.5, 1.0 \} \). For the NPSC data, we use 30000 features and \( \alpha = 0.01 \). For the GPSC data, we use 100000 features and \( \alpha = 0.1 \). For the ParlVote data, we use 100000 features and \( \alpha = 0.01 \).
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Acknowledgements
This work is done as part of the Trondheim Analytica project and funded under Digital Transformation program at Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway.
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Doan, T.M., Kille, B., Gulla, J.A. (2022). Using Language Models for Classifying the Party Affiliation of Political Texts. In: Rosso, P., Basile, V., Martínez, R., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2022. Lecture Notes in Computer Science, vol 13286. Springer, Cham. https://doi.org/10.1007/978-3-031-08473-7_35
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