Staying informed: supervised and semi-supervised multi-view topical analysis of ideological perspective
EMNLP(2010)
摘要
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.
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关键词
metropolis-hasting inference algorithm,efficient inference,semi-supervised multi-view topical analysis,various evaluation,factored topic model,various document collection,ideological perspective,collapsed gibbs,document collection,ideological bias,posterior inference,metropolis hastings,col,gibbs sampling
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