文献类型：会议论文

英文题名：FinPTA: An Effective Model for Financial Sentiment Analysis

作者：Yang, Chen[1,2];Yi, Xiao[1,2];Fan, Guisheng[1,2];Yu, Huiqun[1,2];Zhang, Hengrun[1,2]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China;[2]Shanghai Engn Res Ctr Smart Energy, Shanghai, Peoples R China

会议论文集：29th International Conference on Engineering of Complex Computer Systems-ICECCS

会议日期：JUL 02-04, 2025

会议地点：Hangzhou, PEOPLES R CHINA

语种：英文

外文关键词：Financial Text; Sentiment Analysis; PLMs; Transformer; Adversarial Training

摘要：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.