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

arXiv 2410.12473 ↗First posted 16 Oct 2024cs.IR cs.AIcited by 0

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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.

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.

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References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  6. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 385
  7. 2013Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Textscited by 310
  8. 2019SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimizationcited by 254
  9. 2015Effective LSTMs for Target-Dependent Sentiment Classificationcited by 82
  10. 2021Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applicationscited by 2
  11. 2023CrudeBERT: Applying Economic Theory towards fine-tuning Transformer-based Sentiment Analysis Models to the Crude Oil Marketcited by 1

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