News media platforms publish articles about daily events letting their users comment on them, and forming interesting discussions in almost real-time. To keep users always active and interested, media platforms need an effective recommender system to bring up new articles that match user interests. In this article, we show that we can improve the quality of recommendation by exploiting valuable information provided by user comments. This information reveals aspects not directly tackled by the news article on which they have been posted. We call such aspects latent aspects. We demonstrate how these latent aspects can make a crucial difference in the accuracy of future recommendation. The challenge in detecting them is due to the noisy nature of user comments. To support our claim, we propose a novel news recommendation system that (1) enriches the description of news articles by latent aspects extracted from user comments, (2) deals with noisy comments by proposing a model for user comments ranking, and (3) proposes a diversification model to remove redundancies and provide a wide coverage of aspects. We have tested our approach using large collections of real user activities in four news Web sites, namely The INDEPENDENT, The Telegraph, CNN and Al-Jazeera. The results show that our approach outperforms baseline approaches achieving a significantly higher accuracy.