LDALDA is a generative probabilistic topic model. It represents the documents as a random mixtures of topics over the latent topic space, where each topic is characterized by a distribution over a dictionary of words. LDA and its extensions are ineffective when used with short documents (texts). Issues are coming from: ineffective word relation induction and difficulties with distinguishing ambiguous... More is a generative probabilistic topic model. It represents the documents as a random mixtures of topics over the latent topic space, where each topic is characterized by a distribution over a dictionary of words. LDALDA is a generative probabilistic topic model. It represents the documents as a random mixtures of topics over the latent topic space, where each topic is characterized by a distribution over a dictionary of words. LDA and its extensions are ineffective when used with short documents (texts). Issues are coming from: ineffective word relation induction and difficulties with distinguishing ambiguous... More and its extensions are ineffective when used with short documents (texts). Issues are coming from: ineffective word relation induction and difficulties with distinguishing ambiguous words.