Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification

• URL: http://arxiv.org/abs/2508.03181v1
• Date: Tue, 05 Aug 2025 07:44:42 GMT
• Title: Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification
• Authors: Lukas Pätz, Moritz Beyer, Jannik Späth, Lasse Bohlen, Patrick Zschech, Mathias Kraus, Julian Rosenberger,
• Abstract summary: This study investigates political discourse in the German parliament, the Bundestag, by analyzing approximately 28,000 parliamentary speeches.<n>Two machine learning models for topic and sentiment classification were developed and trained on a manually labeled dataset.<n>The models showed strong classification performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.94 for topic classification.
• Score: 3.3595341706248876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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