[gov2001-l] Preliminary Abstract

Geoff Humphreys and Chris Long Classfying Political Documents In recent years, political methodologists, have produced innumerable automated document classification systems. Many of these systems, such as those based on the well-known Naive Bayes algorithm, treat each word as a distinct entity, ignoring complex interactions between them. While for some applications this approach may appear reasonable, the precise arrangements of words in political documents often convey meanings which cannot be captured so easily. In this paper, we investigate the success of such naive algorithms by comparing Wordscores, a Naive Bayes derivative, to several well-known algorithms and a new classification system based on vanilla recursive heirarchical Dirichlet-multinomial mixture models, pointing out avenues for future advancement. Surprisingly, we find that the assumptions of Wordscores notwithstanding, it shows dramatically increased performance, comparable to some of the latest developments in document classification, at carrying out a small number of carefully selected classifications on meticuously arranged collections of political documents, and discuss its use in practical applications. _______________________________________________ gov2001-l mailing list gov2001-l(a)lists.fas.harvard.edu http://lists.fas.harvard.edu/mailman/listinfo/gov2001-l