Abstract

http://ssrn.com/abstract=1026925
 
 

References (23)



 
 

Citations (1)



 


 



Ideology Classifiers for Political Speech


Bei Yu


Kellogg School of Management

Stefan Kaufmann


affiliation not provided to SSRN

Daniel Diermeier


Northwestern University - Kellogg School of Management

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.

Number of Pages in PDF File: 30

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

working papers series





Download This Paper

Date posted: November 2, 2007  

Suggested Citation

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

Contact Information

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
Feedback to SSRN


Paper statistics
Abstract Views: 1,277
Downloads: 230
Download Rank: 78,372
References:  23
Citations:  1

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo2 in 0.390 seconds