Scaling Text with the Class Affinity Model

TL;DR

This paper introduces a probabilistic text modeling framework to estimate latent ideological positions of legislators, capturing nuanced political spectra beyond simple vote analysis, validated through word influence measures and bootstrap uncertainty quantification.

Contribution

The paper develops a novel text scaling method that models latent positions on a spectrum and validates it with word influence analysis and bootstrap-based uncertainty estimates.

Findings

Successfully scaled Irish legislators' ideological positions.

Revealed speech nuances not captured by votes or party lines.

Quantified uncertainty in ideological estimates.

Abstract

Probabilistic methods for classifying text form a rich tradition in machine learning and natural language processing. For many important problems, however, class prediction is uninteresting because the class is known, and instead the focus shifts to estimating latent quantities related to the text, such as affect or ideology. We focus on one such problem of interest, estimating the ideological positions of 55 Irish legislators in the 1991 D\'ail confidence vote. To solve the D\'ail scaling problem and others like it, we develop a text modeling framework that allows actors to take latent positions on a "gray" spectrum between "black" and "white" polar opposites. We are able to validate results from this model by measuring the influences exhibited by individual words, and we are able to quantify the uncertainty in the scaling estimates by using a sentence-level block bootstrap. Applying our method to the D\'ail debate, we are able to scale the legislators between extreme pro-government and pro-opposition in a way that reveals nuances in their speeches not captured by their votes or party affiliations.