用石油市场专用语言模型提升油价情感分析精度
Unifying Economic and Language Models for Enhanced Sentiment Analysis of the Oil Market
- 基于GPT改进的CrudeBERT专精原油领域术语理解
- 情感评分与WTI期货走势相关性显著提升
- 适合金融量化、能源投资等需要情绪预测的场景
原油作为全球经济关键组成部分，其价格受经济趋势、政治事件和自然灾害等多种因素影响。传统基于历史数据的预测方法存在局限，而自然语言处理技术的发展为基于事件的分析带来新可能。特别是语言模型（LM）及其先进版本生成式预训练变换器（GPT）在海量自然语言分类中展现潜力，但普遍难以处理领域特定术语，限制了其在原油领域的应用效果。为弥补这一差距，本文提出专为原油市场设计的微调语言模型CrudeBERT。实验结果表明，CrudeBERT的情感评分与WTI期货曲线拟合度更高，显著提升了价格预测能力，凸显将经济原理融入语言模型的关键作用。
原文摘要 · Abstract (English)
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.
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