---
title: Machine Learning vs. Dictionary for Sentiment
url: https://www.ml-quant.com/papers/repec/inm-ormnsc-v-68-y-2022-i-7-p-5514-5532/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:inm:ormnsc:v:68:y:2022:i:7:p:5514-5532
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fmnsc.2021.4156%3Bh%3Drepec%3Ainm%3Aormnsc%3Av%3A68%3Ay%3A2022%3Ai%3A7%3Ap%3A5514-5532
featured: 2023-08-24
citations: unknown
topic: LLMs & Text
---


# Machine Learning vs. Dictionary for Sentiment

Machine-learning methods, particularly the random-forest-regression-tree method, significantly improve the capture of disclosure sentiment at 10-K filing and conference-call dates compared to dictionary-based measures.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fdx.doi.org%2F10.1287%2Fmnsc.2021.4156%3Bh%3Drepec%3Ainm%3Aormnsc%3Av%3A68%3Ay%3A2022%3Ai%3A7%3Ap%3A5514-5532
- Identifier: RePEc:inm:ormnsc:v:68:y:2022:i:7:p:5514-5532
- Released: 2022-05-10
- First featured: Quant Letter No. 13 (2023-08-24): https://www.ml-quant.com/issues/2023-08-24/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

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