Staying Informed: Supervised and Semi-Supervised Multi-view Topical Analysis of Ideological Perspective
Abstract
With the proliferation of user-generated articles over the web, it becomes imperative to develop automated methods that are aware of the
ideological-bias implicit in a document collection. While there exist methods that can
classify the ideological bias of a given document, little has been done toward understanding 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 various evaluations and illustrations of the utility of our model on various document collections with promising results. Finally we give a
Metropolis-Hasting inference algorithm for a
semi-supervised extension with decent results.
ideological-bias implicit in a document collection. While there exist methods that can
classify the ideological bias of a given document, little has been done toward understanding 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 various evaluations and illustrations of the utility of our model on various document collections with promising results. Finally we give a
Metropolis-Hasting inference algorithm for a
semi-supervised extension with decent results.
