The Issue-Adjusted Ideal Point Model
Machine Learning
2012-09-27 v1 Machine Learning
Applications
Abstract
We develop a model of issue-specific voting behavior. This model can be used to explore lawmakers' personal voting patterns of voting by issue area, providing an exploratory window into how the language of the law is correlated with political support. We derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we demonstrate both improvement in heldout prediction performance and the model's utility in interpreting an inherently multi-dimensional space.
Keywords
Cite
@article{arxiv.1209.6004,
title = {The Issue-Adjusted Ideal Point Model},
author = {Sean M. Gerrish and David M. Blei},
journal= {arXiv preprint arXiv:1209.6004},
year = {2012}
}