FinancialBERT-Sentiment-Analysis
| Property | Value |
|---|---|
| Author | Ahmed Rachid Hazourli |
| Training Dataset | Financial PhraseBank (4840 samples) |
| Architecture | BERT-based with financial domain adaptation |
| Performance | 98% weighted average F1-score |
What is FinancialBERT-Sentiment-Analysis?
FinancialBERT-Sentiment-Analysis is a specialized BERT model designed for sentiment analysis in financial texts. Pre-trained on extensive financial corpora and fine-tuned on the Financial PhraseBank dataset, it classifies text into three sentiment categories: positive, neutral, and negative with exceptional accuracy.
Implementation Details
The model was fine-tuned using carefully selected hyperparameters: learning rate of 2e-5, batch size of 32, maximum sequence length of 512, and trained for 5 epochs. It achieves impressive metrics with precision and recall both averaging around 0.98 across all sentiment classes.
- Pre-trained on financial domain texts for specialized understanding
- Fine-tuned on 4,840 financially-oriented sentences
- Implements 3-way classification (positive, neutral, negative)
- Achieves 96-98% precision across all categories
Core Capabilities
- Accurate sentiment classification of financial texts
- Handles complex financial terminology and context
- Supports batch processing of multiple sentences
- Easy integration via Hugging Face Transformers pipeline
Frequently Asked Questions
Q: What makes this model unique?
The model combines domain-specific pre-training on financial texts with targeted fine-tuning for sentiment analysis, resulting in superior performance compared to general-purpose BERT models in financial applications.
Q: What are the recommended use cases?
The model is ideal for analyzing financial news, reports, and statements for sentiment analysis, market analysis, and automated financial text processing. It's particularly effective for organizations needing to process large volumes of financial text data.
