Measuring news sentiment

By A. Shapiro, Moritz Sudhof, Daniel J. Wilson. Journal of Econometrics, 2020

438 citations recorded by the source corpus.

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 highlight 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 two applications to the economic research on sentiment. First, we show that daily news sentiment is predictive of movements of survey-based measures of consumer sentiment. Second, motivated by Barsky and Sims (2012), we estimate the impulse responses of macroeconomic variables to sentiment shocks, finding that positive sentiment shocks increase consumption, output, and interest rates and dampen inflation.

Subfields: Economics and Econometrics, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a35c82f4de182fcd6f94947284b583a56ae0a41b

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Citations in the corpus

The Time Variation in Risk Appetite and Uncertainty (2019, positive): “In Panel C, the OECD business con ﬁ dence index is − 0.36 correlated with our risk aversion index, whereas the news based sentiment measure of Shapiro et al. (2020) is − 0.49 correlated with risk aversion in Panel D.”