2017-01 | June 2018
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Measuring News Sentiment
This paper demonstrates state-of-the-art text sentiment analysis tools while developing a new time-series measure of economic sentiment derived from economic and financial newspaper articles from January 1980 to April 2015. We compare the predictive accuracy of a large set of sentiment analysis models using a sample of articles that have been rated by humans on a positivity/negativity scale. The results high-light the gains from combining existing lexicons and from accounting for negation. We also generate our own sentiment-scoring model, which includes a new lexicon built specifically to capture the sentiment in economic news articles. This model is shown to have better predictive accuracy than existing, “off-the-shelf”, models. Lastly, we provide an application to the economic research on sentiment. Motivated by Barsky and Sims (2012), we estimate the impulse responses of macroeconomic variables to sentiment shocks. Our results are consistent with their theoretical and empirical predictions. Positive sentiment shocks increase consumption, output, and interest rates and dampen inflation.
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Shapiro, Adam Hale, Moritz Sudhof, and Daniel Wilson. 2017. "Measuring News Sentiment," Federal Reserve Bank of San Francisco Working Paper 2017-01. Available at https://doi.org/10.24148/wp2017-01