Financial Sentiment Classification with BERT
Fine-tuned and compared transformer models for classifying sentiment in financial text.
- NLP
- Machine Learning
TL;DR
A study comparing several transformer architectures for financial sentiment classification, with careful evaluation and error analysis aimed at practical, explainable NLP. (Placeholder — replace with your real narrative.)
Problem
Financial text is short, domain-specific, and full of subtle cues; generic sentiment models misread it. The goal was to find a model that is accurate and practical to deploy.
Note: Placeholder case-study content — replace with your real write-up.
Why it matters
Sentiment signals from news and filings feed into trading, risk, and research workflows. A model that is both accurate and cheap to run is far more useful in practice than a marginally better but expensive one.
Dataset / inputs
A labeled financial-sentiment dataset (positive / neutral / negative). (Placeholder — name the dataset, sizes, and class balance. Confirm licensing before publishing.)
Technical decisions
I fine-tuned three architectures to map the accuracy/cost frontier. DeBERTa was the strongest on F1, while DistilRoBERTa offered most of the accuracy at a fraction of the latency — the better choice for a live demo. (Placeholder — replace with your real numbers.)
Challenges
Class imbalance and ambiguous “neutral” examples were the main sources of error. Reading the confusion matrix and individual misclassifications was the most informative part of the project.
Methods
- Fine-tuned BERT, DistilRoBERTa, and DeBERTa with Hugging Face Trainer
- Stratified train/val/test split with fixed seeds
- Class-weighted loss for label imbalance (placeholder)
Results
- Model comparison table with accuracy / F1 / latency trade-offs (placeholder)
- Error analysis on misclassified examples (placeholder)
Lessons learned
- Smaller distilled models can be close to larger ones at much lower cost
- Error analysis surfaces label noise and ambiguous examples
Limitations
- Dataset domain may limit generalization to other financial text
- No calibration analysis yet
Next steps
- Add probability calibration and confidence thresholds
- Package the best model as a Hugging Face Space demo