Machine Learning Methods for texts from Political Science

Abstract: In the WASP-HS project "Bias and Methods of AI Technology Studying Political Behavior" we are investigating and developing machine learning methods to help political scientists study the enormous amounts of text documents that are otherwise beyond manual analysis, such as the document repository from the Swedish Riksdag. This is a collaborative project between Dr. Annika Fredén's group in the Political Science department at Lund University, and the group of Dr. Moa Johansson at Computer Science at Chalmers.

I will give an overview of some of the work so far on how we are trying to highlight differences in language use between parties in the Swedish Riksdag. The first paper is about comparing word embeddings trained on texts from different parties. The second concerns explainability of text classification: if a machine learning algorithm can classify text as belonging to one party or another, it is useful for a social scientist to know what such a classification is based on. We have started to develop a new method for class explainability for text for this purpose. This is going work with PhD student Denitsa Saynova, and post-doc Bastiaan Bruinsma.

machine learning

Audience: researchers in the discipline

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.