We develop a novel embedding-based model for predicting legislative roll-call votes from bill text. The model introduces multidimensional \textit{ideal vectors} for legislators as an alternative to single dimensional ideal point models for quantitatively analyzing roll-call data. These vectors are learned to correspond with pre-trained word embeddings which allows us to analyze which features in a bill text are most predictive of political support. Our model is quite simple, while at the same time allowing us to successfully predict legislator votes on specific bills with higher accuracy than past methods.