Aspect-based Sentiment Analysis in Document -- FOMC Meeting Minutes on Economic Projection

TL;DR

This paper introduces a weakly supervised aspect-based sentiment analysis model tailored for financial documents, specifically FOMC meeting minutes, to improve economic projections by extracting sentiments on various aspects.

Contribution

The paper presents a novel weak supervision approach for ABSA in financial texts, enabling analysis without large labeled datasets.

Findings

Model effectively extracts aspect-based sentiments from financial documents.

Sentiment analysis correlates with macroeconomic indicators.

Approach advances financial text analysis methods.

Abstract

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.