Humanities

Computational thematics: comparing algorithms for clustering the genres of literary fiction

O. Sobchuk and A. Šeļa

This innovative study by Oleg Sobchuk and Artjoms Šeļa delves into the realm of unsupervised learning algorithms, unveiling how they can be leveraged to automatically cluster literary genres. Through a meticulous comparison of text preprocessing, feature extraction, and distance measurement methods on a diverse corpus, the authors discern the most effective techniques in genre classification, promising insights for bibliophiles and machine learning enthusiasts alike.