  The paper presents a new training dataset of sentences in 7 languages,
manually annotated for sentiment, which are used in a series of experiments
focused on training a robust sentiment identifier for parliamentary
proceedings. The paper additionally introduces the first domain-specific
multilingual transformer language model for political science applications,
which was additionally pre-trained on 1.72 billion words from parliamentary
proceedings of 27 European parliaments. We present experiments demonstrating
how the additional pre-training on parliamentary data can significantly improve
the model downstream performance, in our case, sentiment identification in
parliamentary proceedings. We further show that our multilingual model performs
very well on languages not seen during fine-tuning, and that additional
fine-tuning data from other languages significantly improves the target
parliament's results. The paper makes an important contribution to multiple
disciplines inside the social sciences, and bridges them with computer science
and computational linguistics. Lastly, the resulting fine-tuned language model
sets up a more robust approach to sentiment analysis of political texts across
languages, which allows scholars to study political sentiment from a
comparative perspective using standardized tools and techniques.
