id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_o3t2tz3njne23esc3tfvrtet5i	Kevin M. Quinn	How to Analyze Political Attention with Minimal Assumptions and Costs	2010	20	.pdf	application/pdf	16509	3726	76	statistical learning model that uses word choices to infer topical categories covered in a set of speeches and to identify the We use the topic model to examine the agenda in the U.S. Senate from 1997 to Using a new database of over 118,000 speeches (70,000,000 words) from the Congressional Record, our model reveals words in a text to a topic category is allowed to be highly language choice and (2) a daily data series of attention to different topics in the U.S. Senate from 1997 to learning, and the topic model we describe here—fill distinctive niches as tools for political science. For example, one reader of a specific journal article might attempt to place that article into one of a set of substantive categories (e.g., legislative studies / agenda setting / methodology / text analysis), while another reader stage (reading, human coding), our topic model requires Applying the Topic Model to U.S. Senate Speech, 1995–2004	./cache/work_o3t2tz3njne23esc3tfvrtet5i.pdf	./txt/work_o3t2tz3njne23esc3tfvrtet5i.txt