15:30 - 17:45
Thursday-Panel
Chair/s:
Akitaka Matsuo
Discussant/s:
Musashi Harukawa
Meeting Room C

Tom Paskhalis
Record Linkage with Text: Merging Data Sets When Information is Limited

Akitaka Matsuo, Kentaro Fukumoto
Legislators’ Sentiment Analysis Supervised by Legislators

Lukas Stoetzer, Heike Klüver
Measuring Parties' Evolving Issue Agendas

Hauke Licht
Cross-lingual supervised classification of political texts

Anna Palau, Andreu Casas, Luz Muñoz
Who is Effective at Amending Legislation? A Text Reuse Analysis of Which Amendments Make it into Law
Measuring Parties' Evolving Issue Agendas
Lukas Stoetzer, Heike Klüver
Humboldt University of Berlin

Political parties emphasis different issues in their public communication efforts to address the topics of the day and to strengthen their policy profiles. In this article, we develop a dynamic supervised machine learning model to measure parties' evolving issue agendas from press releases. The model combines a language model for the classification of texts into pre-specified issue categories and a dynamic hierarchical model for the evolution of each parties' emphasis of these issue-categories in published press-releases. We estimate the model jointly on the labelled and unlabeled press-release data to evaluate the overall issue agendas. A simulation study shows that this model outperforms existing approaches to estimate category prevalence in text corpora when issue agendas evolve smoothly over time. An application to press releases of German and Austrian parties from 2012-2018 illustrates the usefulnesses of our approach in studying dynamic party competition, communication and behaviour.