This paper describes automatic topic spotting of literary texts based on the Russian short stories corpus, compiling stories written in the first third of the XXth century.
Non-negative matrix factorization (NMF) is a valuable alternative to existing approaches of dynamic topic modeling and it can find niche topics and related vocabularies that are not captured by existent methods. The experiments were conducted on text samples extracted from the corpus, the given samples contain texts of 300 different authors. This approach allows to trace the topic dynamics of Russian prose for 30 years — from 1900
to 1930.