Digital news has revolutionized information consumption which had offered the instant access to global events through social media , websites and mobile apps . The rapid expansion of digital news media has led to an increased need for automated sentiment analysis to assess public opinions and detect biases. This research allow us to explore sentiment analysis in the news articles using natural language processing (NLPs) techniques. Various approaches, including lexicon-based models, are discussed, with a focus on their effectiveness in way to capture the sentiment nuances in journalistic text. The research focuses on analyzing the sentiment of news articles using nlp techniques to determine whether the content is positive, negative, or neutral. Preprocessing techniques such as tokenization, stopword removal, and lemmatization are applied to enhance the accuracy. This research also tries to explore for detecting the sentiment analysis of regional language news article whether the content is positive , negative or neutral . Results indicate that advanced nlp models achieve high accuracy, making sentiment analysis valuable for media monitoring as well as in decision-making