Abstract
This study investigates political discourse in the German parliament, the Bundestag, by analyzing approximately 28,000 parliamentary speeches from the last five years. Two machine learning models for topic and sentiment classification were developed and trained on a manually labeled dataset. The models showed strong classification performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.94 for topic classification (average across topics) and 0.89 for sentiment classification. Both models were applied to assess topic trends and sentiment distributions across political parties and over time. The analysis reveals remarkable relationships between parties and their role in parliament. In particular, a change in style can be observed for parties moving from government to opposition. While ideological positions matter, governing responsibilities also shape discourse. The analysis directly addresses key questions about the evolution of topics, sentiment dynamics, and party-specific discourse strategies in the Bundestag.
Abstract (translated)
这项研究通过对过去五年内约28,000篇德国联邦议院（Bundestag）的议会演讲进行分析，探讨了政治话语。为此开发并训练了两个用于主题和情感分类的机器学习模型，这些模型是在手动标记的数据集上训练出来的。在评估中，这两个模型表现出色：主题分类的受试者操作特征曲线下面积（AUROC）为0.94（平均值），情感分类则为0.89。 这两种模型被用于评估各政党及其随时间变化的主题趋势和情感分布情况。分析结果揭示了党派之间令人瞩目的关系以及他们在议会中的角色作用。尤其是，从执政党转变为反对党的过程中可以看到明显的风格转变。虽然意识形态立场很重要，但治理责任也塑造着政治话语的内容。 这项研究直接回答了关于主题演变、情感动态变化及各政党特定的议会议程策略在联邦议院中如何发展的关键问题。
URL
https://arxiv.org/abs/2508.03181
