PLTA-FinBERT: Pseudo-Label Generation-Based Test-Time Adaptation for Financial Sentiment Analysis

Financial sentiment analysis leverages natural language processing techniques to quantitatively assess sentiment polarity and emotional tendencies in financial texts. Its practical application in investment decision-making and risk management faces two major challenges: the scarcity of high-quality labeled data due to expert annotation costs, and semantic drift caused by the continuous evolution of market language. To address these issues, this study proposes PLTA-FinBERT, a pseudo-label generation-based test-time adaptation framework that enables dynamic self-learning without requiring additional labeled data. The framework consists of two modules: a multi-perturbation pseudo-label generation mechanism that enhances label reliability through consistency voting and confidence-based filtering, and a test-time dynamic adaptation strategy that iteratively updates model parameters based on high-confidence pseudo-labels, allowing the model to continuously adapt to new linguistic patterns. PLTA-FinBERT achieves 0.8288 accuracy on the sentiment classification dataset of financial sentiment analysis, representing an absolute improvement of 2.37 percentage points over the benchmark. On the FiQA sentiment intensity prediction task, it obtains an R2 of 0.58, surpassing the previous state-of-the-art by 3 percentage points.