Paper: Staying Informed: Supervised and Semi-Supervised Multi-View Topical Analysis of Ideological Perspective

ACL ID D10-1111
Title Staying Informed: Supervised and Semi-Supervised Multi-View Topical Analysis of Ideological Perspective
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

With the proliferation of user-generated arti- cles over the web, it becomes imperative to de- velop automated methods that are aware of the ideological-bias implicit in a document col- lection. While there exist methods that can classify the ideological bias of a given docu- ment, little has been done toward understand- ing the nature of this bias on a topical-level. In this paper we address the problem of modeling ideological perspective on a topical level using a factored topic model. We develop efficient inference algorithms using Collapsed Gibbs sampling for posterior inference, and give var- ious evaluations and illustrations of the util- ity of our model on various document collec- tions with promising results. Finally we give a Metropolis-Hasting inference algorithm for a semi-supe...