Published Paper Details:

In today's era of information overload, accessing relevant and meaningful news has become increasingly challenging. This research presents a Personalized News Aggregator with Sentiment Analysis--a system designed to deliver user-centric news content tailored to individual interests and preferences. The platform aggregates news from diverse sources and leverages Natural Language Processing (NLP) techniques to analyze the sentiment of each article, helping users better understand the emotional tone and context of the information they consume. The system integrates machine learning models for sentiment analysis with recommendation algorithms to enable personalized content delivery. By offering features such as filtering, keyword search, and sentiment-based categorization, the solution addresses limitations found in traditional news platforms. This paper explores the technical implementation of the system, including data collection, preprocessing, model selection, and web-based deployment. It also highlights the system's potential in enhancing information accessibility and improving user satisfaction through a more refined, relevant, and engaging news experience.