An Approach to Social News Recommendation based on Focused Crawling and Sentiment Analysis

News recommendation poses several specific challenges compared to other domains, such as freshness and serendipity. The proposed research will develop new methods and techniques to address some of such challenges. With the aim of handling the users' changing interests and the fast evolution overtime of news, my solution will be proposed in the social network domain, exploiting an adaptive focused crawling algorithm. Moreover, it will consider a given user's attitude towards her interests, with the purpose of recommending articles in line with her beliefs. An experimental evaluation is currently being implemented to assess the effectiveness of my approach, also in comparison with state-of-the-art techniques.