Stable topic modeling for web science

Abstract

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.

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