Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data
Description
Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model
