This project examines target-level financial sentiment analysis of news headlines for stock companies like Amazon, Netflix, Nvidia, and Alphabet. It compares traditional sentiment analysis methods with advanced large language models, using a curated Bloomberg Terminal dataset to understand how financial news sentiment affects market perceptions.

This study explores target-level sentiment analysis in financial news for stock-listed firms (Alphabet, Amazon, Netflix, Nvidia), benchmarking advanced generative LLMs against lexicon-based and discriminative transformer-based models. Using a curated Bloomberg Terminal dataset, it analyzes how financial news sentiment shapes market perception