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EMNLP
2010

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

8 years 5 months ago
Staying Informed: Supervised and Semi-Supervised Multi-View Topical Analysis of Ideological Perspective
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.
Amr Ahmed, Eric P. Xing
Added 11 Feb 2011
Updated 11 Feb 2011
Type Journal
Year 2010
Where EMNLP
Authors Amr Ahmed, Eric P. Xing
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