LLMs and Generative AI for Financial Sentiment Classification: An Explainable Domain-Adaptive Framework

This study aims to investigate the integration of generative artificial intelligence (GAI) and advanced large language models (LLMs) in financial sentiment research, focusing on improving the accuracy and robustness of financial sentiment classification from investor-generated textual data. The research employs advanced large language models, including XLNet, FinBERT, T5, Gemma-7B, Llama-2, and Llama-3, specifically fine-tuned to address the intricacies of financial language. We utilize generative AI models, such as GPT-4, GPT-3.5, and GPT-2, for data augmentation to mitigate scarcity. The fine-tuned Gemma-7b model proved to be the most successful, with a greater Success Rate (S-rate). The Gemma-7b model showed significant enhancements in performance after fine-tuning, highlighting its capacity to grasp the intricacies of financial emotion. This methodology provides a robust framework for financial sentiment classification and supports the extraction of meaningful sentiment signals from financial text. The results demonstrate the effectiveness of advanced LLMs for financial sentiment analysis and highlight their potential for supporting future research and analytical applications in financial text mining.