A tensor based approach for temporal topic modeling

Abstract

Latent Dirichlet Allocation (LDA) are a suite of algorithms that are often used for topic modeling. We study the statistical model behind LDA and review how tensor methods can be used for learning LDA, as well as implement a variation of an already existing method. Next, we present an innovative algorithm for temporal topic modeling and provide a new dataset for learning topic models over time. Last, we create a visualization for the word-topic probabilities.