From Concordance to Inference: ParlaCAP Helps ParlaMint Escape the Linguistics Lab

Abstract

ParlaCAP is an OSCARS Open Science cascading grant project aimed at extending the use of the ParlaMint parliamentary corpora beyond corpus linguistics into the wider Social Sciences and Humanities (SSH). While ParlaMint provides a rich, comparable collection of parliamentary debates and accompanying metadata, its broader uptake has been limited. ParlaCAP addresses this by enriching the data with automatically derived political agendas and sentiment, enabling new forms of comparative political analysis. Using recent advances in multilingual transformer models, the project annotates over 8 million speeches from 28 European parliaments in more than 20 languages. By integrating ParlaMint with the Comparative Agendas Project (CAP) coding schema, ParlaCAP produces a FAIR dataset suitable for cross-national research on interaction of policy, sentiment, and political identity. The enrichments rely on two models, XLM-R-ParlaSent and XLM-R-ParlaCAP, both performing comparably to human annotators. The latter is trained using a teacher–student approach, where GPT-4o-generated labels are used to fine-tune a scalable classifier. The dataset is available via the CROSSDA repository and a user-friendly API. The talk concludes with a series of use cases demonstrating how meaningful insights can be obtained with minimal technical effort.

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