Beyond Keywords: Leveraging Generative LLMs and Label Aggregation to Classify Economic Policy Uncertainty in News Articles

This research describes the adaptation of large language models (LLMs) for economic monitoring in the public sector to automatically determine whether an article discusses economic policy uncertainty (EPU) and to identify its specific type. Previous studies either rely on keywords, which often result in a high number of false positives, or use machine learning (ML) approaches that require a large amount of high-quality human-labeled data, which is costly and time-consuming to obtain. In this study, we propose approaches based on weak supervision, using generative LLMs to create synthetic labels through prompting, making the approach both cost-effective and scalable. In addition, we propose methods for multi-label and hierarchical classification of articles related to EPU.