Identifying groups with opposite stances using link-based categorization

Abstract:

This thesis proposes a link-based approach to identify supporting and opposing groups in a Weblog community. We formulate the interaction behavior as a graph. Bloggers involved in the discussion of one specific issue are formulated as vertices. Semantic orientation is used to construct possible opposite opinion links. Bloggers with opposite stances will form an opposite link. A max-cut algorithm is used latter to obtain the optimal approximation of supporting and opposing groups. The categorization results are compared between semantic orientation classifier and simple link-based categorization. The simple link-based categorization compares then with the enhancement of link-based categorization using hypergraph.

Keywords: hypergraph, categorization, semantic orientation, link, max-cut

Date of defense: 2005-7-12