Classifying Party Affiliation from Political Speech

  title={Classifying Party Affiliation from Political Speech},
  author={Diermeier and Bei Yu and Stefan Kaufmann and Daniel Diermeier},
In this article, we discuss the design of party classifiers for Congressional speech data. We then examine these party classifiers’ person-dependency and time-dependency. We found that party classifiers trained on 2005 House speeches can be generalized to the Senate speeches of the same year, but not vice versa. The classifiers trained on 2005 House speeches performed better on Senate speeches from recent years than on older ones, which indicates the classifiers’ time-dependency. This… CONTINUE READING
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Changing minds? Not in Congress

  • K. T. Poole
  • Public Choice,
  • 2007

A bibliography on sentiment classification

  • A. Esuli
  • Retrieved October, 31, 2007 from http:// liinwww…
  • 2006
1 Excerpt

An automated method of topic-coding legislative speech over time with application to the 105th-108th U.S. Senate

  • K. M. Quinn, B. L. Monroe, +4 authors April
  • Paper presented at the annual meeting of the…
  • 2006
1 Excerpt

E-rulemaking: Issues in current research and practice

  • S. W. Shulman
  • International Journal of Public Administration…
  • 2005
2 Excerpts

Partial - birth abortion ban act of 2003

  • S. W. Shulman
  • 2005
1 Excerpt

Academic obsessions and classification realities: Ignoring practicalities in supervised classification

  • D. J. Hand
  • D. Banks, L. House, F. R. McMorris, P. Arabie…
  • 2004
1 Excerpt

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