Measuring News Sentiment
Abstract: 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.
Keywords: news; text sentiment analysis; economic sentiment; sentiment-scoring; sentiment shocks;
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Provider: Federal Reserve Bank of San Francisco
Part of Series: Working Paper Series
Publication Date: 2020-03-13
Note: First published 1/5/2017 as "Measuring News Sentiment", FRSBF ER Working Paper 2017-01. This updated version, published under same title and WP number, on 3/13/2020.