Learning alignments from legislative discourse

Daniel Kauffman, Foaad Khosmood, Toshihiro Kuboi and Alex Dekhtyar
Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age
Delft, Netherlands

In this work, we seek to quantify the extent to which a legislator's spoken language indicates their degree of alignment toward an organization that has a taken a documented position on some legislation. To perform this study, we use a corpus of bill discussion transcripts provided by Digital Democracy1. We then apply proven learning methods in the field of natural language processing to predict alignment scores between each member of the California state legislature and a select set of state-recognized organizations.