Sentinel: An Integrated Framework for News Sentiment Analysis, Bias Detection, and Coverage Comparison Using LLMs

The sheer proliferation of news sources online has resulted in a splintered media space, where readers are more often than not exposed to skewed reporting and limited perspectives. We present Sentinel, an AI-powered framework that relies on Large Language Models (LLMs) to facilitate sentiment analysis, bias detection, and coverage comparison. This would integrate an understanding of the emotional undertone, if biased at all, and the depth of coverage within all these news stories for the consumers. Our system incorporates web scraping, advanced NLP techniques, and custom algorithms to provide a comprehensive view on news articles. Going through the architecture, methodology, and real-world applications of the Sentinel, that is a product which has in it all the potential to empower its users in making ever better-informed decisions in the increasingly polarized media environment.