Hierarchical comments-based clustering

Information resources on the Web like videos, images, and documents are increasingly becoming more “social” through user engagement via commenting systems. These commenting systems provide a forum for users to discuss the resources but have the side effect of providing valuable editorial and contextual information about the resources. In this paper, we explore a comments-driven clustering framework for organizing Web resources according to this user-based perspective. Concretely, we propose a hierarchical comment clustering approach that relies on two key features: (i) comment term normalization and key term extraction for distilling noisy comments for effective clustering; and (ii) a real-time insertion component for incrementally updating the comments-based hierarchy so that resources can be efficiently placed in the hierarchy as comments arise and without the need to re-generate the (potentially) expensive hierarchy. We study the clustering approach over the popular video shar...

• Type: Journal
• Year: 2011
• Where: SAC
• Authors: Chiao-Fang Hsu, James Caverlee, Elham Khabiri
• Keywords: Applied Computing, SAC 2011, Term Normalization, Time Insertion, YouTube