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Ideology Classifiers for Political Speech

30 Pages Posted: 2 Nov 2007  

Date Written: November 1, 2007

Abstract

In this paper we discuss the design of ideology classifiers for Congressional speech data. We then examine the ideology classifiers' person-dependency and time-dependency. We found that ideology classifiers trained on 2005 House speeches can be generalized to the Senate speeches of the same year, but not vice versa. The ideology classifiers trained on 2005 House speeches predict recent year Senate speeches better than older speeches, which indicates the classifiers' time-dependency. This dependency may be caused by changes in the issue agenda or the ideological composition of Congress.

Keywords: machine learning, text classification, generalizability, ideology, evaluation

Suggested Citation

Yu, Bei and Kaufmann, Stefan and Diermeier, Daniel, Ideology Classifiers for Political Speech (November 1, 2007). Available at SSRN: https://ssrn.com/abstract=1026925 or http://dx.doi.org/10.2139/ssrn.1026925

Bei Yu

Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Stefan Kaufmann

affiliation not provided to SSRN ( email )

No Address Available

Daniel Diermeier (Contact Author)

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

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