Sentiment analysis of financial news helps traders and investors assess market sentiment by analyzing the tone and context of news articles, reports, and social media discussions. Using Natural Language Processing (NLP) and machine learning models, sentiment analysis categorizes financial news into positive, negative, or neutral sentiments, providing insights into market trends and investor behavior. Techniques like lexicon-based analysis, which uses predefined dictionaries of sentiment-laden words, and machine learning-based methods, such as transformers (BERT, GPT), help extract meaningful insights. These models process financial terms, economic indicators, and geopolitical news to detect emotions and biases in the text. Sentiment scores derived from financial news can enhance algorithmic trading, risk management, and portfolio strategies. For example, a rise in negative sentiment regarding inflation or interest rates may signal potential market downturns. However, challenges include sarcasm detection, context misinterpretation, and rapidly changing news cycles. Despite these hurdles, sentiment analysis remains a vital tool for financial decision-making.