CrudeBERT
| Property | Value |
|---|---|
| Author | Captain-1337 |
| Base Model | FinBERT |
| Paper | CrudeBERT: Applying Economic Theory towards fine-tuning Transformer-based Sentiment Analysis Models to the Crude Oil Market |
| Repository | https://github.com/Captain-1337/Master-Thesis |
What is CrudeBERT?
CrudeBERT is a specialized language model developed during a master's thesis project, designed specifically for analyzing sentiment in crude oil-related news headlines. It represents a significant advancement in domain-specific sentiment analysis by fine-tuning FinBERT with a focus on oil market dynamics. The model incorporates economic theory, particularly supply and demand principles, to better understand and predict crude oil price movements based on news sentiment.
Implementation Details
The model employs domain adaptation techniques to specifically target crude oil market sentiment. It processes news headlines through a BERT-based architecture, producing sentiment classifications (positive, negative, neutral) that are specifically calibrated for the oil market context. The implementation includes careful consideration of supply and demand factors that influence oil prices, making it more accurate for this specific domain than general-purpose sentiment models.
- Based on FinBERT architecture with domain-specific fine-tuning
- Processes news headlines with maximum length of 64 tokens
- Outputs three-way sentiment classification
- Includes specialized preprocessing for oil market terminology
Core Capabilities
- Sentiment analysis specifically calibrated for crude oil market news
- Integration of economic theory in sentiment classification
- Processing of headlines from reputable sources
- Domain-adapted sentiment analysis for WTI crude oil price movements
- Support for batch processing of multiple headlines
Frequently Asked Questions
Q: What makes this model unique?
CrudeBERT stands out through its specialized domain adaptation for the crude oil market. Unlike general financial sentiment models, it incorporates specific understanding of how supply and demand changes affect oil prices, making it more accurate for oil-related sentiment analysis than general-purpose models like FinBERT.
Q: What are the recommended use cases?
The model is best suited for analyzing news headlines related to crude oil markets, particularly for understanding potential price movements based on supply and demand signals in news content. It's especially useful for researchers and analysts studying the relationship between news sentiment and oil price movements.
