Publication Type
Working Paper
Version
publishedVersion
Publication Date
12-2022
Abstract
We examine whether empirical results using text-based sentiment of U.S. annual reports depend on the underlying context, within documents, from which sentiment is measured. We construct a clause-level measure of context, showing that sentiment is driven by many different contexts and that positive and negative sentiment are driven by different contexts. We then construct context-level sentiment measures and examine whether sentiment works as expected at the context-level across four prediction problems. Our results demonstrate that document-level sentiment exhibits significant noise in prediction and suggest that document-level aggregation of sentiment leads to missed empirical nuances. The contexts driving sentiment results vary substantially by outcome, suggesting lower empirical internal validity for document-level sentiment. Using three additional sentiment measures, we document the same inferences, concluding that document-level aggregation likely leads to lower internal validity. Sentiment is thus best applied at the level of specific contexts rather than across whole documents.
Keywords
Sentiment analysis, context, machine learning, aggregation, lasso regression, text analysis
Discipline
Accounting | Finance and Financial Management | Numerical Analysis and Scientific Computing
Research Areas
Corporate Reporting and Disclosure
Citation
Richard M.CROWLEY and WONG, M.H. Franco.
Understanding sentiment through context. (2022).
Available at: https://ink.library.smu.edu.sg/soa_research/1990
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Accounting Commons, Finance and Financial Management Commons, Numerical Analysis and Scientific Computing Commons