  In the following paper, we use a topic modeling algorithm and sentiment
scoring methods to construct a novel metric that serves as a leading indicator
in recession prediction models. We hypothesize that the inclusion of such a
sentiment indicator, derived purely from unstructured news data, will improve
our capabilities to forecast future recessions because it provides a direct
measure of the polarity of the information consumers and producers are exposed
to. We go on to show that the inclusion of our proposed news sentiment
indicator, with traditional sentiment data, such as the Michigan Index of
Consumer Sentiment and the Purchasing Manager's Index, and common factors
derived from a large panel of economic and financial indicators helps improve
model performance significantly.
