# Unifying Economic and Language Models for Enhanced Sentiment Analysis of the Oil Market

arXiv: https://arxiv.org/abs/2410.12473 · HTML: https://mlsanity.com/paper/2410.12473/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019)
- [Effective LSTMs for Target-Dependent Sentiment Classification](https://mlsanity.com/paper/1512.01100/) (2015)
- [Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications](https://mlsanity.com/paper/2102.00827/) (2021)
- [CrudeBERT: Applying Economic Theory towards fine-tuning Transformer-based Sentiment Analysis Models to the Crude Oil Market](https://mlsanity.com/paper/2305.06140/) (2023)

## Abstract (arXiv)

Crude oil, a critical component of the global economy, has its prices influenced by various factors such as economic trends, political events, and natural disasters. Traditional prediction methods based on historical data have their limits in forecasting, but recent advancements in natural language processing bring new possibilities for event-based analysis. In particular, Language Models (LM) and their advancement, the Generative Pre-trained Transformer (GPT), have shown potential in classifying vast amounts of natural language. However, these LMs often have difficulty with domain-specific terminology, limiting their effectiveness in the crude oil sector. Addressing this gap, we introduce CrudeBERT, a fine-tuned LM specifically for the crude oil market. The results indicate that CrudeBERT's sentiment scores align more closely with the WTI Futures curve and significantly enhance price predictions, underscoring the crucial role of integrating economic principles into LMs.