用机器学习分析德国议会演讲，揭示政党话语随执政状态变化的规律
Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification
- 基于2.8万篇演讲构建标注数据集，训练主题与情感分类模型
- 主题分类AUROC达0.94，情感分类达0.89，表现优异
- 发现执政党转向在野后话语风格显著改变，责任影响表达方式
本研究通过分析过去五年约2.8万篇德国联邦议院（Bundestag）的议会演讲，探究政治话语特征。基于人工标注数据集，开发并训练了两个机器学习模型，分别用于主题和情感分类。模型在主题分类上的平均AUROC达到0.94，在情感分类上达到0.89，表现出色。将模型应用于政党间及时间维度的分析，揭示出政党角色与话语策略之间的显著关联。特别发现，政党从执政转为在野时，其话语风格发生明显转变。尽管意识形态立场重要，但执政职责本身也深刻影响言论内容与形式。该研究直接回应了关于议题演变、情绪动态及政党话语策略的核心问题。
原文摘要 · Abstract (English)
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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