Understanding sentiment through context

• Main Authors: Richard M.CROWLEY, WONG, M.H. Franco
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Sentiment analysis; context; machine learning; aggregation; lasso regression; text analysis; Accounting; Finance and Financial Management; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/soa_research/1990; https://ink.library.smu.edu.sg/context/soa_research/article/3017/viewcontent/SSRN_id4316229.pdf
• Institution: Singapore Management University

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.