Słowa kluczowe
topic modelling
social sciences
social welfare
automated text analysis
Jak cytować
Abstrakt
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.
Finansowanie
This work was supported by the National Science Centre, Poland, under research project “Social welfare in the light of topic modelling: A preliminary study”, no 2021/05/X/HS6/00067.
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Licencja
W przypadku zakwalifikowania tekstu do druku Autor wyraża zgodę na przekazanie praw autorskich do tego artykułu wydawcy (zob. Polityka open access). Autor artykułu zachowuje prawo wykorzystania treści opublikowanego przez czasopismo artykułu w dalszej pracy naukowej i popularyzatorskiej pod warunkiem wskazania źródła publikacji.