Leveraging Financial Sentiment Analysis for Strategic Business Decision-Making

Abstract

This paper explores the application of fine-tuned Llama 2 for enhanced financial sentiment analysis, aiming to generate actionable strategic business insights. By leveraging financial data from sources such as news articles, social media, and corporate reports, the model is fine-tuned to classify sentiment with improved accuracy. The study compares Llama 2's performance with traditional sentiment analysis models and highlights its superior ability to predict market movements, manage risk, and inform investment strategies. Additionally, the paper discusses the challenges of domain-specific data preprocessing and the implications for business decision-making.