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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. In this paper, authors analyze sparsity of the posterior parameters in LDA using a variational Bayesian algorithm. They derive an expression for the VB free energy which shows its asymptotic behaviour with respect to number of words (N), number of documents (M), vocabulary size (L) etc. Their results suggest that, for certain settings of L,M,N, the sparsity behaviour changes drastically at a particular hyper-parameter setting. These changes differ from those of MAP and partial-Bayes algorithms. The problem discussed in this paper is original, interesting, and is perhaps useful too.