Description

• Title:
  Epistemological aspect of topic modelling in the social sciences: Latent Dirichlet Allocation
• Creator:
  Baranowski, Mariusz
• Description:
  Aware of the challenges faced by the social sciences in publishing a massive volume of research papers, it is worth looking at a novel but no longer so new ways of machine learning for the purposes of literature review. To this end, I explore a probabilistic topic model called Latent Dirichlet Allocation (LDA) in the context of the epistemological challenge of analysing texts on social welfare. This paper aims to describe how the LDA algorithm works for large corpora of data, along with its advantages and disadvantages. This preliminary characterisation of an inductive method for automated text analysis is intended to give a brief overview of how LDA can be used in the social sciences.
• Object availability:
  free access
• Rights:
  Prawa autorskie (c) 2022 Mariusz Baranowski
• Date:
  2022.08.21
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion;
  wstęp
• Language:
  English;
  eng
• Source:
  Critical Review; Vol. 4 No. 1 (2022); 7-16;
  Przegląd Krytyczny; Tom 4 Nr 1 (2022); 7-16;
  2657-8964
• Format:
  application/pdf
• Publisher:
  Adam Mickiewicz University Poznan
• Relation: http://pressto.amu.edu.pl/index.php/pk/article/view/34992/30237
• Subject:
  Latent Dirichlet Allocation (LDA);
  topic modelling;
  social sciences;
  social welfare;
  automated text analysis
• Identifier: http://pressto.amu.edu.pl/index.php/pk/article/view/34992
• Data provider:
  Platforma Otwartych Czasopism Naukowych UAM
• Can I use it?:
  seek permission
• Type:
  text