| This dissertation analyzes the Congressional modification of legislation and the political activity of nonprofits by making use of new machine-learning based approaches to analyzing text. First, I develop a novel method for document summary on the tensor product of vector word embeddings that captures information about local co-occurrence, presented in chapter 1. This provides the basis for a new measure of the legislative activity of Congressional actors that is employed in chapters 2 and 3 to investigate agenda setting and principal agent dynamics in legislative drafting. This analysis makes use of a new dataset of multiple versions each bill as it is modified by Congress for 1993-2014. In chapter 4, I identify the substantive dimensions that underly ideological differences in policy proposals over time by projecting legislative text onto concepts of interest such as Federal- versus state- control. Finally, in joint work with Drew Dimmery presented in chapter 5, I provide a new approach to identifying political activities by nonprofits by making predictions using the text from the websites of 339,818 organizations, and validate our results with crowd-sourced human judgements. In addition to their direct substantive implications, these studies demonstrate new approaches to analyzing text that can be applied broadly by political scientists and social scientists. |
