Legislative speech records from the 101st to 108th Congresses of the US Senate are analysed to study political ideologies. A widely-used text classification algorithm – Support Vector Machines (SVM) – allows the extraction of terms that are most indicative of conservative and liberal positions in legislative speeches and the prediction of senators’ ideological positions, with a 92 per cent level of accuracy. Feature analysis identifies the terms associated with conservative and liberal ideologies. The results demonstrate that cultural references appear more important than economic references in distinguishing conservative from liberal congressional speeches, calling into question the common economic interpretation of ideological differences in the US Congress.
(Online publication May 23 2011)
* Department of Managerial Economics and Decision Sciences (MEDS) and Ford Motor Company Center for Global Citizenship, Kellogg School of Management and Northwestern Institute on Complex Systems (NICO), Northwestern University (email: [email protected]); Department of Political Science, University of Montreal; School of Information Studies, Syracuse University; and Department of Linguistics, Northwestern University, respectively. The authors wish to thank seminar participants at the annual meetings of the American Political Science Association and the Midwest Political Science Association, as well as the members of the Institutions, Organizations and Growth research group at the Canadian Institute for Advanced Research (CIFAR) for their helpful comments. Financial support from the Ford Motor Company Center for Global Citizenship, Kellogg School of Management, Northwestern University, and CIFAR is gratefully acknowledged.