标题： 高级深度学习技术在分析财报电话会议 transcripts 中的方法与应用 显示英文标题

标题： Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications

摘要： 本研究对用于收益电话会议记录情感分析的深度学习方法（如 BERT、FinBERT 和 ULMFiT）进行了比较分析。 目标是探讨如何利用自然语言处理 (NLP) 从大规模金融记录中提取情感，从而帮助做出更明智的投资决策和风险管理策略。 我们根据金融情感分析的背景，检查每个模型的优点和局限性，重点是数据预处理要求、计算效率和模型优化。 通过严格的实验，我们使用准确率、精确度、召回率和 F1 值等关键指标评估它们的表现。 此外，我们讨论了可能的改进措施，以提高这些模型在金融文本分析中的有效性，并提供了关于其在现实世界金融决策中适用性的见解。 显示英文摘要

摘要： 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.