Hierarchical comments-based clustering
Conference Paper
Overview
Identity
Additional Document Info
Other
View All
Overview
abstract
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 sharing site YouTube. YouTube is a challenging and difficult environment, notorious for its extremely short, ill-formed, and often unintelligible user-contributed comments. Through extensive experimental study, we find that the proposed approach can lead to effective and efficient comments-based video organizing even in a YouTube-like environment. 2011 ACM.
name of conference
Proceedings of the 2011 ACM Symposium on Applied Computing
