In this paper we present a methodology designed to improve the intelligent personalization of news services. Our methodology integrates textual content analysis tasks to achieve an elaborate user model, which represents separately short-term needs and long-term multi-topic interests. The characterization of user’s interests includes his preferences about content, using a wide coverage and non-specific-domain classification of topics, and structure (newspaper sections). The application of implicit feedback allows a proper and dynamic personalization.