Supervised cross-domain topic classification trains a model on a labeled source corpus and applies it to an unlabeled target corpus from a different domain. This approach leverages existing labeled data to reduce effort compared with collecting new within-domain training data, while offering clearer, research-targeted topics than unsupervised methods.

🔍 The Approach

• An algorithm is trained to classify topics in a labeled source corpus and then extrapolates those topic labels to documents in an unlabeled target corpus from another domain.

📚 Data Used: Party Platforms to Parliamentary Speeches

• Source corpus: labeled party platforms.
• Target corpus: unlabeled parliamentary speeches.

🧪 How Performance Was Evaluated

• Standard within-domain error metrics were reported.
• Cross-domain performance received additional validation by manually labeling a subset of the target-corpus documents to compare against classifier assignments.

📈 Key Findings

• The classifier can accurately assign topics in parliamentary speeches.
• Accuracy varies substantially by topic, indicating some topics transfer better across domains than others.
• Using existing labeled data makes this method substantially more efficient than training new within-domain supervised models.
• Compared with unsupervised topic models, the supervised cross-domain method can be more precisely targeted to a research question and yields topics that are easier to validate and interpret.

⚙️ Tools and Applications

• Diagnostic tools are proposed to evaluate when cross-domain classification will perform well and to identify problematic topics.
• Two case studies illustrate substantive use: how electoral rules and the gender of parliamentarians influence the choice of speech topics.

💡 Why It Matters

• Enables reuse of labeled resources to extend topic measurement across domains, saving time and improving interpretability.
• Provides a practical workflow and diagnostics for researchers studying political texts across different institutional contexts.