A Deep Learning Model to Predict Congressional Roll Call Votes from Legislative Texts


Jonathan Wayne Korn and Mark A. Newman, Harrisburg University, USA


Developments in natural language processing (NLP) techniques, convolutional neural networks (CNNs), and long-short- term memory networks (LSTMs) allow for a state-of-the-art automated system capable of predicting the status (pass/fail) of congressional roll call votes. The paper introduces a custom hybrid model labeled "Predict Text Classification Network" (PTCN), which inputs legislation and outputs a prediction of the document's classification (pass/fail). The convolutional layers and the LSTM layers automatically recognize features from the input data's latent space. The PTCN's custom architecture provides elements enabling adaptation to the input's variance from adjustment to the kernel weights over time. On the document level, the model reported an average evaluation of 67.32% using 10-fold crossvalidation. The results suggest that the model can recognize congressional voting behaviors from the associated legislation's language. Overall, the PTCN provides a solution with competitive performance to related systems targeting congressional roll call votes.


Deep Learning (DL), Convolutional Neural Networks (CNNs), Long-Short-Term Memory Networks (LSTMs), Natural Language Processing (NLP), Congressional Roll Call Votes