Sentiment Analysis of News Headlines for Stock Market Prediction using VADER

In today’s world, everyone, from novices to experts, seeks to understand and profit from the complex stock market. However, predicting stock price movements remains a daunting task due to its frequent fluctuations. Companies worldwide rely on these predictions to boost profits. This study focuses on the immediate impact of news headlines on a company’s stock performance. Using Natural Language Processing (NLP), this study compares two methods: one using traditional machine learning algorithms and the other leveraging VADER sentiment analysis. These methods assess whether news headlines are positive, negative, or neutral and their influence on stock prices. Our research highlights the superior accuracy of the VADER-based approach in predicting stock market trends, offering a more reliable tool for investors and businesses in navigating the ever-changing financial landscape.