The Journal of Machine Learning Research
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Discovering voter preferences in blogs using mixtures of topic models
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
Reading the markets: forecasting public opinion of political candidates by news analysis
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Predicting risk from financial reports with regression
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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A U. S. Congressional bill is a textual artifact that must pass through a series of hurdles to become a law. In this paper, we focus on one of the most precarious and least understood stages in a bill's life: its consideration, behind closed doors, by a Congressional committee. We construct predictive models of whether a bill will survive committee, starting with a strong, novel baseline that uses features of the bill's sponsor and the committee it is referred to. We augment the model with information from the contents of bills, comparing different hypotheses about how a committee decides a bill's fate. These models give significant reductions in prediction error and highlight the importance of bill substance in explanations of policy-making and agenda-setting.