Aspect-based Sentiment Analysis in Document -- FOMC Meeting Minutes on Economic Projection
This addresses the problem of extracting nuanced sentiments from financial documents like FOMC minutes for economic forecasting, but it appears incremental as it applies existing weak supervision techniques to a specific domain.
The paper tackles the lack of labeled data for aspect-based sentiment analysis in financial texts by proposing a weakly supervised model, and it analyzes the model's predictive power on macroeconomic indicators, though no concrete numbers are provided.
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.