Predicting Left-Right Positions from Hand-Coded Content Analysis using Machine Learning

Abstract

The Manifesto Project’s widely used left-right index of party policy positions (RILE), built from human-coded sentences from party manifestos, can be predicted using machine learning. We demonstrate this using some simple classifiers to show that using these conservative approaches as a baseline, performance is already as good as human coders. It works in multiple languages. Using transfer learning, we also show how a model trained on coded manifesto sentences can be used on new texts to predict left-right positions, and validate these with independent survey-based evidence.

Patrick J. Chester

Assistant Professor

Patrick Chester is an Assistant Professor of Computational Social Science at Stevens Institute of Technology. He received his PhD in Politics from New York University’s Politics Department.