Authors: Allison Koh, Daniel Boey, and Hannah Bechara
Hand-labeled political texts are often required in empirical studies on party systems, coalition building, agenda setting, and many other areas in political science research. While hand-labeling remains the standard procedure for analyzing political texts, it can be slow and expensive, and subject to human error and disagreement. Recent studies in the field have leveraged supervised machine learning techniques to automate the labeling process of electoral programs, debate motions, and other relevant documents. We build on current approaches to label shorter texts and phrases in party manifestos using a pre-existing coding scheme developed by political scientists for classifying texts by policy domain and policy preference. Using labels and data compiled by the Manifesto Project, we make use of the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) in conjunction with Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) to seek out the best model architecture for policy domain and policy preference classification. We find that our proposed BERT-CNN model outperforms other approaches for the task of classifying statements from English language party manifestos by major policy domain.