Work

Word embeddings on ideology and issues from Swedish parliamentarians’ motions: a comparative approach

Year: 2024

Type: article

Abstract: Quantitative analysis of large-scale political text data in the form of word embeddings has great potential for systematising differences between political parties. We examine the differences between embeddings obtained from speakers from the two competitors for the PM position in Sweden (Social Democrats and Moderates) over a 30-year period. The goal is to compare how off-the-shelf general pre-trained models perform relative to pre-training on a smaller dataset from the same domain. In the analysis, we focus on two types of concepts: issues and ideological terms. We find that generally, the off-the-shelf pre-trained models lead to more reliable results and greater emphasis on ideological differences between the studied parties.

Source: Journal of Elections Public Opinion and Parties

DOI: 10.1080/17457289.2024.2433979

Authors: Annika Fredén, Moa Johansson, Denitsa Saynova

Institutions: Lund University, Chalmers University of Technology

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Funders: American Political Science Association, Vetenskapsrådet, Marcus och Amalia Wallenbergs minnesfond

Award: NAISS