Topic models for short documents Inspired by this question, I'm wondering whether any work has been done on topic models for large collections of extremely short texts. My intuition is that Twitter should be a natural inspiration for such models. However, from some limited experimentation, it looks like standard topic models (LDA, etc) perform quite poorly on this kind of data.
Does anyone out there know of any work which has been done in this area? This paper talks about applying LDA to Twitter, but I'm really interested in whether there are other algorithms which perform better in the short-document context.
 A: This is a late answer, but it can be useful for other people searching for related research and tools for this problem:


*

*Weiwei Guo from Columbia implemented code for short-text topic modeling. He described the implementation in the paper "Modeling Sentences in the Latent Space" (http://aclweb.org/anthology-new/P/P12/P12-1091v2.pdf) and the code is available here:
http://www.cs.columbia.edu/~weiwei/code.html

*Although this is not topic modeling, if you have a classification task involving short pieces of texts, you can use LibShortText. From their web site description 
"LibShortText is an open source tool for short-text classification and analysis. It can handle the classification of, for example, titles, questions, sentences, and short messages..."
http://www.csie.ntu.edu.tw/~cjlin/libshorttext/
A: While I'm not super familiar with his work, I know Jacob Eisenstein has done work in text analysis and graphical models in twitter data. In particular, this paper describes an application of topic modeling in twitter data and microblogs.
Edit: actually after reading the paper a bit more, they state: 

However, the average
  message on Twitter is only sixteen word tokens,
  which is too sparse for traditional topic modeling; instead, we gathered together all of the messages
  from a given user into a single document.

So perhaps that very paper may not be of much help, still maybe other Eisenstein publications may lead you in the right direction.
A: A recent paper called "a biterm topic model for short text" (WWW13) has made some progress on this topic, and here is its code 
