In today's world, where information holds significant power and data is a key driver in the stock market, this work explores how computer methods can be used to analyze emotions and monitor changes in news articles concerning the stock market. The project explores leveraging computer methods to analyze emotions and monitor changes in news articles concerning the stock market. It integrates Amazon Web Services (AWS) technologies, including AWS SageMaker, API Gateway, and a Telegram API, to offer users an efficient means of engaging with the stock market. Through a Telegram bot interface, users can communicate stock preferences, triggering AWS SageMaker functions via API Gateway. The system, developed in Python, fetches real-time stock prices and conducts sentiment analysis on market news, providing users with actionable insights for trading decisions. The Telegram bot offers tailored intraday trading suggestions based on real-time data and analysis. The project utilizes natural language processing (NLP) techniques and libraries such as yfinance, newspaper, transformers, and scipy.special to analyze sentiment and volatility in news articles, aiding in intraday investment decisions. It emphasizes NLP's potential in understanding public sentiment and financial markets.