This project focuses on developing a News Sentiment Classification System that predicts whether a news article carries a positive or negative sentiment. Motivated by research suggesting that negative news can adversely affect readers’ mental well-being, the system aims to help users identify the nature of news before reading it. The project utilizes web crawling techniques to collect news articles from a major English-language newspaper in Nepal and applies machine learning algorithms such as Logistic Regression and Naïve Bayes for sentiment classification. Text preprocessing techniques, including Porter’s Algorithm, are used to improve classification accuracy. Testing with training and test datasets demonstrated classification accuracies of 73% and 80% for Logistic Regression and Naïve Bayes respectively. The system provides users with article links and emoticons representing the detected sentiment, promoting a more mindful news-reading experience.