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Financial sentiment analysis with FUNNEL : filtered UNion for NER-based ensemble labeling

Nordansjö, William LU ; Fourong, Fredrik and Qasim, Muhammad LU (2025) In Digital Finance 7(4). p.725-744
Abstract

This paper introduces FUNNEL (Filtered UNion for NER-based Ensemble Labeling), a novel ensemble-based framework for labeling financial news that enhances the reliability of stock-specific sentiment signals. The framework integrates weak keyword-based heuristics with a transformer-based named entity recognition model (spaCy) through a weighted voting scheme, balancing precision and recall. Manual evaluation of 1,400 article–label pairs demonstrates that FUNNEL outperforms the original FNSPID labels in both accuracy and coverage. Applied across seven major companies, the framework reveals systematic differences in sentiment signals produced by FinBERT, RoBERTa, and VADER. These results indicate that integrating different labeling... (More)

This paper introduces FUNNEL (Filtered UNion for NER-based Ensemble Labeling), a novel ensemble-based framework for labeling financial news that enhances the reliability of stock-specific sentiment signals. The framework integrates weak keyword-based heuristics with a transformer-based named entity recognition model (spaCy) through a weighted voting scheme, balancing precision and recall. Manual evaluation of 1,400 article–label pairs demonstrates that FUNNEL outperforms the original FNSPID labels in both accuracy and coverage. Applied across seven major companies, the framework reveals systematic differences in sentiment signals produced by FinBERT, RoBERTa, and VADER. These results indicate that integrating different labeling strategies enhances dataset reliability, coverage, and stability, providing a scalable framework for financial sentiment analysis.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Deep learning, FUNNEL, Labeling, Natural language processing, Sentiment analysis, Transformer models
in
Digital Finance
volume
7
issue
4
pages
20 pages
publisher
Springer International Publishing
external identifiers
  • scopus:105020287797
ISSN
2524-6984
DOI
10.1007/s42521-025-00162-3
language
English
LU publication?
yes
id
71d557c9-dde6-4e26-b4d0-995e6d1e895c
date added to LUP
2025-12-10 09:50:03
date last changed
2025-12-10 09:51:23
@article{71d557c9-dde6-4e26-b4d0-995e6d1e895c,
  abstract     = {{<p>This paper introduces FUNNEL (Filtered UNion for NER-based Ensemble Labeling), a novel ensemble-based framework for labeling financial news that enhances the reliability of stock-specific sentiment signals. The framework integrates weak keyword-based heuristics with a transformer-based named entity recognition model (spaCy) through a weighted voting scheme, balancing precision and recall. Manual evaluation of 1,400 article–label pairs demonstrates that FUNNEL outperforms the original FNSPID labels in both accuracy and coverage. Applied across seven major companies, the framework reveals systematic differences in sentiment signals produced by FinBERT, RoBERTa, and VADER. These results indicate that integrating different labeling strategies enhances dataset reliability, coverage, and stability, providing a scalable framework for financial sentiment analysis.</p>}},
  author       = {{Nordansjö, William and Fourong, Fredrik and Qasim, Muhammad}},
  issn         = {{2524-6984}},
  keywords     = {{Deep learning; FUNNEL; Labeling; Natural language processing; Sentiment analysis; Transformer models}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{725--744}},
  publisher    = {{Springer International Publishing}},
  series       = {{Digital Finance}},
  title        = {{Financial sentiment analysis with FUNNEL : filtered UNion for NER-based ensemble labeling}},
  url          = {{http://dx.doi.org/10.1007/s42521-025-00162-3}},
  doi          = {{10.1007/s42521-025-00162-3}},
  volume       = {{7}},
  year         = {{2025}},
}