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

Summary, in English

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.

• Author: Annika Fredén; Moa Johansson; Denitsa Saynova
• Department/s: LU Profile Area: Natural and Artificial Cognition; Department of Political Science
• Publishing year: 2024-12-04
• Language: English
• Publication/Series: Journal of Elections, Public Opinion and Parties
• Links: http://dx.doi.org/10.1080/17457289.2024.2433979
• Document type: Journal article
• Publisher: Routledge
• Topic: Political Science; Computer and Information Science
• Keywords: machine learning; Parliaments; text as data; word embeddings; word embeddings; parties; ideology
• Status: Inpress
• Project: Bias and methods of AI technology studying political behavior
• ISBN/ISSN/Other: ISSN: 1745-7289