  This study proposes a new method of incorporating emotions from newspaper
articles into macroeconomic forecasts, attempting to forecast industrial
production and consumer prices leveraging narrative and sentiment from global
newspapers. For the most part, existing research includes positive and negative
tone only to improve macroeconomic forecasts, focusing predominantly on large
economies such as the US. These works use mainly anglophone sources of
narrative, thus not capturing the entire complexity of the multitude of
emotions contained in global news articles. This study expands the existing
body of research by incorporating a wide array of emotions from newspapers
around the world - extracted from the Global Database of Events, Language and
Tone (GDELT) - into macroeconomic forecasts. We present a thematic data
filtering methodology based on a bi-directional long short term memory neural
network (Bi-LSTM) for extracting emotion scores from GDELT and demonstrate its
effectiveness by comparing results for filtered and unfiltered data. We model
industrial production and consumer prices across a diverse range of economies
using an autoregressive framework, and find that including emotions from global
newspapers significantly improves forecasts compared to three autoregressive
benchmark models. We complement our forecasts with an interpretability analysis
on distinct groups of emotions and find that emotions associated with happiness
and anger have the strongest predictive power for the variables we predict.
