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|Abstract:||We develop several predictive models linking legislative sentiment to legislative text. Our models, which draw on ideas from ideal point estimation and topic models, predict voting patterns based on the contents of bills and infer the political leanings of legislators. With supervised topics, we provide an exploratory window into how the language of the law is correlated with political support. We also derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we predict specific voting patterns with high accuracy.|
|Citation:||Gerrish, SM, Blei, DM. (2011). Predicting legislative roll calls from text. Proceedings of the 28th International Conference on Machine Learning, ICML 2011, 489 - 496|
|Pages:||489 - 496|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the 28th International Conference on Machine Learning, ICML 2011|
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