  The need for timely data analysis for economic decisions has prompted most
economists and policy makers to search for non-traditional supplementary
sources of data. In that context, text data is being explored to enrich
traditional data sources because it is easy to collect and highly abundant. Our
work focuses on studying the potential of textual data, in particular news
pieces, for measuring economic policy uncertainty (EPU). Economic policy
uncertainty is defined as the public's inability to predict the outcomes of
their decisions under new policies and future economic fundamentals.
Quantifying EPU is of great importance to policy makers, economists, and
investors since it influences their expectations about the future economic
fundamentals with an impact on their policy, investment and saving decisions.
Most of the previous work using news articles for measuring EPU are either
manual or based on a simple keyword search. Our work proposes a machine
learning based solution involving weak supervision to classify news articles
with regards to economic policy uncertainty. Weak supervision is shown to be an
efficient machine learning paradigm for applying machine learning models in low
resource settings with no or scarce training sets, leveraging domain knowledge
and heuristics. We further generated a weak supervision based EPU index that we
used to conduct extensive econometric analysis along with the Irish
macroeconomic indicators to validate whether our generated index foreshadows
weaker macroeconomic performance
