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 differenttopic 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.
|Title of host publication||WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference|
|Publication status||Published - 2016|