Clustering Social News Based on User Affection

Recently, several news aggregation services have emerged to deal with the problem of information overload and news personalization. These news providers are able to organize news based on content similarity as a strategy to improve the user reading experience. However, organizing news solely on content fails to consider actual human reading behavior, and in turn ignores the importance of user perception in news personalization. We propose an enhanced news clustering technique based on an user affect model, which is a feasible framework for news categorization that can contribute to building more human-centric interactive systems. Empirical results demonstrate the effectiveness of clustering news articles through the enrichment of a user affect model when compared to traditional keyword-based clustering.