Financial sentiment analysis for pre-trained language models incorporating dictionary knowledge and neutral features
Year: 2025
Type: article
Abstract: With increasing financial market complexity, accurate sentiment analysis of financial texts has become crucial. Traditional methods often misinterpret financial terminology and show high error rates in neutral sentiment recognition. This study aims to improve financial sentiment analysis accuracy through developing EnhancedFinSentiBERT, a model incorporating financial domain pre-training, dictionary knowledge embedding, and neutral feature extraction. Experiments on the FinancialPhraseBank, FiQA and Headline datasets demonstrate the model’s superior performance compared to mainstream methods, particularly in neutral sentiment recognition. Ablation analysis reveals that dictionary knowledge embedding and neutral feature extraction contribute most significantly to model improvement.
Authors: Yongyong Sun, Haiping Yuan, Fei Xu
Institution: Xi'an Technological University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,740
OpenAlex ID: W4409725643