Topic modeling is a powerful tool for analyzing large collections of user-generated web content, but it still suffers from problems with topic stability, which are especially important for social sciences. We evaluate stability for different topic models and propose a new model, granulated LDA, that samples short sequences of neighboring words at once. We show that gLDA exhibits very stable results.
['Sergei Koltcov', 'Sergey I. Nikolenko', 'Olessia Koltsova', 'Svetlana Bodrunova']
Stable topic modeling for web science: granulated LDA