Temporal data distribution shift is prevalent in the financial text. How can
a financial sentiment analysis system be trained in a volatile market
environment that can accurately infer sentiment and be robust to temporal data
distribution shifts? In this paper, we conduct an empirical study on the
financial sentiment analysis system under temporal data distribution shifts
using a real-world financial social media dataset that spans three years. We
find that the fine-tuned models suffer from general performance degradation in
the presence of temporal distribution shifts. Furthermore, motivated by the
unique temporal nature of the financial text, we propose a novel method that
combines out-of-distribution detection with time series modeling for temporal
financial sentiment analysis. Experimental results show that the proposed
method enhances the model's capability to adapt to evolving temporal shifts in
a volatile financial market.

金融文本中存在时间数据分布的变化，如何在不稳定的市场环境中训练一个能够准确推断情感且对时间数据分布变化具有鲁棒性的金融情感分析系统？本文通过对跨越三年的真实金融社交媒体数据集进行经验研究，在时间数据分布变化的情况下分析金融情感分析系统的性能下降问题，并基于金融文本的独特时间性质提出了一种结合异常检测和时间序列建模的新方法用于时间金融情感分析。实验证明该方法增强了模型在波动的金融市场中适应不断变化的时间趋势的能力。