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Measuring News Sentiment
We develop and assess new time series measures of economic sentiment based on computational text analysis of economic and financial newspaper articles from January 1980 to April 2015. The text analysis is based on predictive models estimated using machine learning techniques from Kanjoya. We analyze four alternative news sentiment indexes. We find that the news sentiment indexes correlate strongly with contemporaneous business cycle indicators. We also find that innovations to news sentiment predict future economic activity. Furthermore, in most cases, the news sentiment measures outperform ...
News Sentiment in the Time of COVID-19
The COVID-19 pandemic is causing severe disruptions to daily life and economic activity. Reliable assessments of the economic fallout in this rapidly evolving situation require timely data. Existing sentiment indexes are useful indicators of current and future spending but are only available with a lag or have a short history. A new Daily News Sentiment Index provides a way to measure sentiment in real time from 1980 to today. Compared with survey-based measures of consumer sentiment, this index shows an earlier and more pronounced drop in sentiment in recent weeks.