Hyper-community detection in the blogosphere - Nguyen, Thin; Phung, Dinh; Adams, Brett; Tran, Truyen; Venkatesh, Svetha (ACM, 2010)
Most existing work on learning community structure in social network is graph-based whose links among the members are often represented as an adjacency matrix, encoding direct pairwise associations between members. In this paper, we propose a method to group online communities in blogosphere based on the topicslearnt from the content blogged. We then consider a different type of online community formulation - the sentiment-based grouping of online communities. The problem of sentiment-based clustering for community structure discovery is rich with many interesting open aspects to be explored. We propose a novel approach foraddressing hyper-community detection based on users' sentiment. We employ a nonparametric clustering to automatically discover hidden hyper-communities and present the results obtained from a large dataset.