With the advent of outstanding technological progress in the last decade, there has been a remarkable surge in the number of individuals who rely on online news articles as their primary source of information. This exponential growth has prompted news publishers to transition from traditional newspapers to digital platforms, allowing them to reach a wider audience in real-time. As a result, there is now an abundance of news sources publishing a vast quantity of articles on a daily basis. However, this surge in online content has presented a challenge for news readers, as it has become increasingly difficult to access relevant news in real-time, leading to a state of information overload. To tackle this issue, this study proposes the implementation of a user-personalized news categorization and recommendation system that harnesses the power of advanced Natural Language Processing techniques. The suggested model is built upon hybrid filtering, which combines Collaborative Filtering and Content-Based Filtering methodologies to construct a news model that is tailored to each individual user, taking into account their historical news article preferences. Moreover, the model incorporates social media comments from platforms like Reddit to alleviate challenges associated with these techniques, such as the cold-start problem and data sparsity. To evaluate the performance of the system, we conducted assessments using two key metrics: Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (nDCG). The results yielded values of 0.67 and 0.70 for MAP and nDCG, respectively, indicating the effectiveness of the proposed system in providing accurate and relevant news recommendations to users.