Recent advances in Generative AI offer researchers new opportunities to assign topic labels to political and legislative texts without manual coding or programming. The Comparative Agendas Project (CAP) provides a structured topic classification system that captures the major areas of public policy. Traditionally, applying these labels required manual annotation or
Natural Language Processing (NLP) allows us to automatically analyse and categorise textual data. With the help of GenAI, even complex classification tasks can now be executed using just a well-designed prompt. In this post, we present a practical example where GenAI classifies financial-related sentences into domain-specific categories, showcasing how effective
Classifying gender based on names might initially appear straightforward, yet different AI models vary significantly in how accurately and effectively they handle this task. This blog explores the performance of various models when provided with a structured prompt for name-based gender classification using an uploaded .xlsx file. The evaluation specifically
