Classifying Party Affiliation from Political Speech

被引:71
作者
Yu, Bei [1 ,2 ]
Kaufmann, Stefan [3 ]
Diermeier, Daniel [4 ,5 ,6 ,7 ]
机构
[1] Ford Motor Co, Dearborn, MI 48126 USA
[2] Northwestern Univ, Ctr Global Citizenship, Kellogg Sch Management, Evanston, IL 60208 USA
[3] Northwestern Univ, Dept Linguist, Evanston, IL 60208 USA
[4] Dept Managerial Econ & Decis Sci MEDS, Evanston, IL 60208 USA
[5] Ford Motor Co, Ctr Global Citizenship, Dearborn, MI 48126 USA
[6] Kellogg Sch Management
[7] Northwestern Univ, NICO, Evanston, IL 60208 USA
关键词
Machine learning; text classification; generalizability; ideology; evaluation;
D O I
10.1080/19331680802149608
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
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 dependency may be caused by changes in the issue agenda or the ideological composition of Congress.
引用
收藏
页码:33 / 48
页数:16
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