Collective classification of Congressional floor-debate transcripts

Abstract

This paper explores approaches to sentiment classification of U.S. Congressional floor-debate transcripts. Collective classification techniques are used to take advantage of the informal citation structure present in the debates. We use a range of methods based on local and global formulations and introduce novel approaches for incorporating the outputs of machine learners into collective classification algorithms. Our experimental evaluation shows that the mean-field algorithm obtains the best results for the task, significantly outperforming the benchmark technique. © 2011 Association for Computational Linguistics.