title: What's the disadvantage of LDA for short texts?
body: <p>I am trying to understand why Latent Dirichlet Allocation(LDA) performs poorly in short text environments like Twitter. I've read the paper 'A biterm topic model for short text', however, I still do not understand "the sparsity of word co-occurrences". </p>
  
  <p>From my point of view, the generation part of LDA is reasonable for any kind of texts, but what causes bad results in short texts is the sampling procedure. I am guessing LDA samples a topic for a word based on two parts: (1) topics of other words in the same doc (2) topic assignments of other occurrences of this word. Since the (1) part of a short text cannot reflect the true distribution of it, that causes a poor topic assignment for each word.</p>
  
  <p>If you have found this question, please feel free to post your idea and help me understand this.</p>