Supervised Cross-Momentum Contrast: Aligning representations with prototypical examples to enhance financial sentiment analysis

Abstract

Financial sentiment analysis plays a pivotal role in understanding market dynamics and investor sentiment. In this paper, we propose the Supervised Cross-Momentum Contrast (SuCroMoCo) framework, a novel approach for financial sentiment analysis. SuCroMoCo leverages supervised contrastive learning and cross-momentum contrast to align financial text representations with prototypical representations based on sentiment categories. This alignment greatly improves classification performance, addressing the limitations of pre-trained language models (PLMs) in fully grasping the intricate nature of financial text. Through extensive experiments, we demonstrate that SuCroMoCo outperforms existing PLMs-based approaches and Large Language Models (LLMs) on diverse benchmark datasets.