Automated classification of congressional legislation

Abstract

For social science researchers, content analysis and classification of United States Congressional legislative activities have been time consuming and costly. The Library of Congress THOMAS system provides detailed information about bills and laws, but its classification system, the Legislative Indexing Vocabulary (LIV), is geared toward information retrieval instead of the pattern or historical trend recognition that social scientists value. The same event (a bill) may be coded with many subjects at the same time, with little indication of its primary emphasis. In addition, because the LIV system has not been applied to other activities, it cannot be used to compare (for example) legislative issue attention to executive, media, or public issue attention.This paper presents the Congressional Bills Project's (www.congressionalbills.org) automated classification system. This system applies a topic spotting classification algorithm to the task of coding legislative activities into one of 226 subtopic areas. The algorithm uses a traditional bag-of-words document representation, an extensive set of human coded examples, and an exhaustive topic coding system developed for use by the Congressional Bills Project and the Policy Agendas Project (www.policyagendas.org). Experimental results demonstrate that the automated system is about as effective as human assessors, but with significant time and cost savings. The paper concludes by discussing challenges to moving the system into operational use.