U.S. Topical Economic Sentiment
What it measures
Daily sentiment for a range of topics relevant to the U.S. economy, computed from a large news corpus using several lexicon-based methods. The indices are the empirical output of Questioning the news about economic growth (International Journal of Forecasting), which proposes generating thousands of candidate sentiment indices and letting a sparse regression select the ones that forecast — the methodology that became the sentometrics R package.
Winner of the International Journal of Forecasting best paper award 2018–2019.
Coverage
| Frequency | Daily |
| Period | 2 January 1994 – 31 December 2017 |
| Region | United States |
| Series | One value per date, lexicon and topic — long format, 1.37 million rows |
| Format | .csv |
Dates missing from the file mean no news articles were available for that date.
How to cite
Ardia, D., Bluteau, K., Boudt, K. (2019). Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values. International Journal of Forecasting, 35, 1370–1386. doi:10.1016/j.ijforecast.2018.10.010
BibTeX is on the How to cite page.
Terms
Free for academic research. By downloading you agree to cite the reference above, to place
https://sentometrics-research.com in a footnote so others can find the data, and to assume all
risk associated with its use.
Download
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us-topical-economic-sentiment.csv.gz— daily, long format
Gzipped CSV, one header row, ISO 8601 dates. No decompression step needed:
read.csv(gzfile(url("https://sentometrics-research.com/data/us-econ/us-topical-economic-sentiment.csv.gz")))
See the folder README for every column, and what differs from the archived original.
Original as published — the file this is derived from, byte for byte:
Sentometrics_US_Topical_Economic_Sentiment.csv. It will move to a Zenodo deposit with a citable DOI.