Financial Sentiment Analysis

Financial sentiment analysis (FSA) aims to automatically determine the emotional tone expressed in financial text, such as news articles or social media posts, to gauge market sentiment and inform investment decisions. Current research heavily utilizes large language models (LLMs) like BERT, GPT, and their financial variants (e.g., FinBERT), exploring techniques like prompt engineering, knowledge distillation, and instruction tuning to improve accuracy and efficiency, even adapting smaller models to match the performance of larger ones. The field's significance lies in its potential to enhance algorithmic trading strategies, improve risk management, and provide valuable insights into market dynamics for both researchers and financial institutions.