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

arXiv: https://arxiv.org/abs/2508.03181 · HTML: https://mlsanity.com/paper/2508.03181/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Sentiment and position-taking analysis of parliamentary debates: A systematic literature review](https://mlsanity.com/paper/1907.04126/) (2019)
- [Comparison of Topic Modelling Approaches in the Banking Context](https://mlsanity.com/paper/2402.03176/) (2024)
- [CareerBERT: Matching Resumes to ESCO Jobs in a Shared Embedding Space for Generic Job Recommendations](https://mlsanity.com/paper/2503.02056/) (2025)
- [A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis](https://mlsanity.com/paper/2210.12883/) (2022)
- [German Parliamentary Corpus (GerParCor)](https://mlsanity.com/paper/2204.10422/) (2022)
- [Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict](https://mlsanity.com/paper/2503.10648/) (2025)

## Built on by (most cited first, 1 of 1)

- [Umm... With Transformers? Insights from Filled Pause Use across Four Slavic Parliaments](https://mlsanity.com/paper/2607.05964/) (2026)

## Abstract (arXiv)

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.