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How to Analyze Political Attention with Minimal Assumptions and Costs

How to Analyze Political Attention with Minimal Assumptions and Costs,Kevin M. Quinn,Burt L. Monroe,Michael Colaresi,Michael H. Crespin,Dragomir R. Ra

How to Analyze Political Attention with Minimal Assumptions and Costs   (Citations: 11)
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Abstract Previous methods of analyzing the substance of political attention have had to make several restrictive assumptions or been prohibitively costly when applied to large-scale political texts. Here, we describe a topic model for legislative speech, a statistical learning model that uses word choices to infer topical categories covered in a set of speeches and to identify the topic of specic speeches. Our method estimates, rather than assumes, the substance of topics, the keywords that identify topics, and the hierarchical nesting of topics. We use the topic model to examine the agenda in the United States Senate from 1997-2004. Using a new database of over 118,000 speeches (70,000,000 words) from the Congressional Record, our model reveals speech topic categories that are both distinctive and meaningfully inter-related, and a richer view of democratic agenda dynamics than had previously been possible. An earlier version of this paper was presented to the Midwest Political Science Association and was awarded
Published in 2009.
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