对比BERT等模型在财报电话会情感分析中的表现，助力投资决策
Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications
- 用BERT、FinBERT、ULMFiT对比分析财报文本情感
- FinBERT在准确率和F1-score上表现最优，达87.3%与86.1%
- 适合金融量化研究者参考模型选型与优化策略
本研究对比了BERT、FinBERT和ULMFiT等深度学习方法在财报电话会文本情感分析中的应用。目标是探索自然语言处理（NLP）如何从大规模金融文本中提取情感信息，以支持更明智的投资决策和风险管理。重点评估各模型在数据预处理需求、计算效率和模型优化方面的优劣。通过严格实验，使用准确率、精确率、召回率和F1分数等指标进行性能评估。结果显示，FinBERT在多项指标上表现最佳，其F1分数达到86.1%，准确率为87.3%。研究还探讨了提升模型有效性的潜在改进方向，为金融文本分析的实际应用提供了可操作的见解。
原文摘要 · Abstract (English)
This study presents a comparative analysis of deep learning methodologies such as BERT, FinBERT and ULMFiT for sentiment analysis of earnings call transcripts. The objective is to investigate how Natural Language Processing (NLP) can be leveraged to extract sentiment from large-scale financial transcripts, thereby aiding in more informed investment decisions and risk management strategies. We examine the strengths and limitations of each model in the context of financial sentiment analysis, focusing on data preprocessing requirements, computational efficiency, and model optimization. Through rigorous experimentation, we evaluate their performance using key metrics, including accuracy, precision, recall, and F1-score. Furthermore, we discuss potential enhancements to improve the effectiveness of these models in financial text analysis, providing insights into their applicability for real-world financial decision-making.
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