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SUMMARY:Moa Johansson (Chalmers University of Technology)
DTSTART:20230504T111500Z
DTEND:20230504T120000Z
DTSTAMP:20260422T155153Z
UID:gbgstats/25
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/gbgstats/25/
 ">Machine Learning Methods for texts from Political Science</a>\nby Moa Jo
 hansson (Chalmers University of Technology) as part of Gothenburg statisti
 cs seminar\n\nLecture held in MVL14.\n\nAbstract\nIn the WASP-HS project "
 Bias and Methods of AI Technology Studying Political Behavior" we are inve
 stigating and developing machine learning methods to help political scient
 ists study the enormous amounts of text documents that are otherwise beyon
 d manual analysis\, such as the document repository from the Swedish Riksd
 ag. 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.\n\nI 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 le
 arning 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 Sa
 ynova\, and post-doc Bastiaan Bruinsma.\n
LOCATION:https://researchseminars.org/talk/gbgstats/25/
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