FinPTA: An Effective Model for Financial Sentiment Analysis

Chen Yang, Xiao Yi, Guisheng Fan, Huiqun Yu, Hengrun Zhang

Abstract: Financial sentiment analysis is crucial for market insights and investment strategies. While pre-trained language models (PLMs) have demonstrated significant advancements in general domains, they struggle with the unique linguistic characteristics of financial texts. Financial PLMs improve financial feature extraction but are hindered by limited and uniform training data, restricting their ability to capture contextual and semantic nuances. To address these challenges, this work proposes FinPTA a novel framework for financial sentiment analysis. Specifically, FinPTA leverages FiLM (Financial Language Model) for domain-specific feature extraction, capturing intricate semantic features embedded in financial texts. After that, the tTransformer, an enhanced transformer encoding model, is employed to better capture contextual relationships within financial texts. Finally, to further enhance robustness and generalization, we incorporate adversarial training, enabling the model to withstand perturbations and ambiguities inherent in financial texts. Experimental results demonstrate that FinPTA achieves state-of-the-art performance across multiple financial datasets, providing a robust and reliable solution for financial sentiment analysis tasks.

External IDs:dblp:conf/iceccs/YangYFYZ25