Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification
This work addresses the problem of understanding topic trends and sentiment dynamics in parliamentary speeches for political scientists and analysts, but it is incremental as it applies existing methods to new data.
This study tackled the problem of analyzing political discourse in the German parliament by developing machine learning models for topic and sentiment classification on 28,000 speeches, achieving AUROC scores of 0.94 for topic and 0.89 for sentiment classification.
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.