Elaborating word embeddings on central keywords related to issues and ideology based on voters’ perceptions of parties, we find that the word embeddings that started from pre-training on a bigger dataset provided clearer ideological differences between the parties. This article has been published in Journal of Elections, Public Opinion and Parties and is co-work between political science, Lund and computer science, Chalmers.
Why It Matters:
The study’s implications go beyond Sweden. It highlights how AI tools can assist political scientists and policymakers worldwide in dissecting complex political discourse, identifying ideological trends, and better understanding voter communication. This approach could offer new ways to track political polarization, predict policy shifts, and even help voters make more informed decisions.
Authors: Annika Fredén, Moa Johansson, Denitsa Saynova
Title: Word embeddings on issues and ideology from Swedish parliamentarians’ motions: A Comparative Approach