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.

翻译：本文提出了一个由7种语言句子构成的新型训练数据集，这些句子经过人工情感标注，并用于一系列实验，旨在为议会程序训练稳健的情感识别器。此外，本文首次引入专门面向政治科学应用的特定领域多语种Transformer语言模型，该模型额外在27个欧洲议会的议会程序数据（含17.2亿词汇）上进行了预训练。我们通过实验表明，在议会数据上的额外预训练能够显著提升模型的下游性能——在本研究中即为议会程序中的情感识别。我们进一步证明，该多语种模型在微调过程中未涉及的语言上表现优异，且来自其他语言的额外微调数据能显著改善目标议会的分析结果。本文对社会科学的多个学科做出了重要贡献，并将其与计算机科学和计算语言学相连接。最后，所得到的微调语言模型为跨语言政治文本的情感分析提供了一种更稳健的方法，使学者能够利用标准化工具和技术从比较视角研究政治情感。