Daily News Sentiment Index
The Daily News Sentiment Index is a high frequency measure of economic sentiment based on lexical analysis of economics-related news articles. The index is described in Buckman, Shapiro, Sudhof, and Wilson (2020) and based on the methodology developed in Shapiro, Sudhof, and Wilson (2020).
The study by Shapiro, Sudhof, and Wilson (2020, hereafter SSW), constructs sentiment scores for economics-related news articles from 16 major U.S. newspapers compiled by the news aggregator service LexisNexis. The newspapers cover all major regions of the country, including some with extensive national coverage such as the New York Times and the Washington Post. SSW selected articles with at least 200 words where LexisNexis identified the article’s topic as “economics” and the country subject as “United States.” Combining publicly available lexicons with a news-specific lexicon constructed by the authors, the study develops a sentiment-scoring model tailored specifically for newspaper articles.
SSW aggregate the individual article scores into a daily time-series measure of news sentiment, relying on a statistical adjustment that accounts for changes over time in the composition of the sample across newspapers. The Daily News Sentiment Index is constructed as a trailing weighted-average of time series, with weights that decline geometrically with the length of time since article publication. The data here will be regularly updated at a weekly frequency.
Chart 1: Daily News Sentiment Index: Historical View
Chart 2: Daily News Sentiment in the Time of COVID-19
Buckman, Shelby R., Adam Hale Shapiro, Moritz Sudhof, and Daniel J. Wilson. 2020. “News Sentiment in the Time of COVID-19.” FRBSF Economic Letter 2020-08 (April 6).
Shapiro, Adam Hale, Moritz Sudhof, and Daniel J. Wilson. 2020. “Measuring News Sentiment.” FRB San Francisco Working Paper 2017-01.
Daily News Sentiment data (Excel document, 372 kb)
Replication code (Zip file 1.8 mb)