文档中的基于方面的情感分析——FOMC会议纪要中的经济预测
Aspect-based Sentiment Analysis in Document -- FOMC Meeting Minutes on Economic Projection
AI总结：
针对金融文本缺乏标注数据的问题，提出在弱监督下训练基于方面的情感分析模型，并分析其对宏观经济指标的预测能力。
摘要：
联邦储备系统内的联邦公开市场委员会负责管理通货膨胀、最大化就业以及稳定利率。会议纪要对市场走势起着重要作用，因为它们提供了对经济复杂性如何被不断重新权衡的全景视角。因此，从大型金融文本中分析和提取各方面的情感以用于经济预测的兴趣日益增长。然而，由于缺乏大规模标注数据集，基于方面的情感分析并未在金融数据上被广泛使用。在本文中，我提出了一种在弱监督下对金融文档训练ABSA的模型，并分析其对各种宏观经济指标的预测能力。
英文摘要：
The Federal Open Market Committee within the Federal Reserve System is responsible for managing inflation, maximizing employment, and stabilizing interest rates. Meeting minutes play an important role for market movements because they provide the birds eye view of how this economic complexity is constantly re-weighed. Therefore, There has been growing interest in analyzing and extracting sentiments on various aspects from large financial texts for economic projection. However, Aspect-based Sentiment Analysis is not widely used on financial data due to the lack of large labeled dataset. In this paper, I propose a model to train ABSA on financial documents under weak supervision and analyze its predictive power on various macroeconomic indicators.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
