  The goal of this paper is to evaluate the informational content of sentiment
extracted from news articles about the state of the economy. We propose a
fine-grained aspect-based sentiment analysis that has two main characteristics:
1) we consider only the text in the article that is semantically dependent on a
term of interest (aspect-based) and, 2) assign a sentiment score to each word
based on a dictionary that we develop for applications in economics and finance
(fine-grained). Our data set includes six large US newspapers, for a total of
over 6.6 million articles and 4.2 billion words. Our findings suggest that
several measures of economic sentiment track closely business cycle
fluctuations and that they are relevant predictors for four major macroeconomic
variables. We find that there are significant improvements in forecasting when
sentiment is considered along with macroeconomic factors. In addition, we also
find that sentiment matters to explains the tails of the probability
distribution across several macroeconomic variables.
