The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Measuring news sentiment
Open accessAbstract
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.
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Cites
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Cited by
Ash, E., & Hansen, S. (2023) · Annual Review of Economics · doi:10.1146/annurev-economics-082222-074352 · Cited by 197
Sentiment trading with large language models
Kirtac, K., & Germano, G. (2024) · Finance Research Letters · doi:10.1016/j.frl.2024.105227 · Cited by 115
Monetary policy shocks and Bitcoin prices
Ma, C., Tian, Y., Hsiao, S., & Deng, L. (2022) · Research in International Business and Finance · doi:10.1016/j.ribaf.2022.101711 · Cited by 34
Using Large Language Models for Qualitative Analysis can Introduce Serious Bias
Ashwin, J., Chhabra, A., & Rao, V. (2026) · Sociological Methods & Research · doi:10.1177/00491241251338246 · Cited by 22
Metadata
- Authors
- Shapiro, A. H., Sudhof, M., & Wilson, D. J.
- Venue
- Journal of Econometrics
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 228
- Issue
- 2
- Pages
- 221-243
- ISSN
- 0304-4076 (ISSN-L)
- OpenAlex
- W2595186334
- Cited by
- 423
- References
- 38
- FWCI
- 14.21
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
