  The Federal Open Market Committee within the Federal Reserve System is
responsible for managing inflation, maximizing employment, and stabilizing
interest rates. Meeting minutes play an important role for market movements
because they provide the birds eye view of how this economic complexity is
constantly re-weighed. Therefore, There has been growing interest in analyzing
and extracting sentiments on various aspects from large financial texts for
economic projection. However, Aspect-based Sentiment Analysis is not widely
used on financial data due to the lack of large labeled dataset. In this paper,
I propose a model to train ABSA on financial documents under weak supervision
and analyze its predictive power on various macroeconomic indicators.
