30 Pages Posted: 2 Nov 2007
Date Written: November 1, 2007
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: 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