<p>I'm experimenting with <a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation" rel="noreferrer">Latent Dirichlet Allocation</a> for topic disambiguation and assignment, and I'm looking for advice.</p>

<ol>
<li>Which program is the "best", where best is some combination of easiest to use, best prior estimation, fast</li>
<li>How do I incorporate my intuitions about topicality.  Let's say I think I know that some items in the corpus are really in the same category, like all articles by the same author.  Can I add that into the analysis?</li>
<li>Any unexpected pitfalls or tips I should know before embarking?</li>
</ol>

<p>I'd prefer is there are R or Python front ends for whatever program, but I expect (and accept) that I'll be dealing with C.  </p>

In addition to the
usual
sources
, it seems like the most active area talking about this is on the
topics-models listserv
.  From my initial survey, the easiest package to understand is the
LDA Matlab package
.
This is not lightweight stuff at all, so I'm not surprised it's hard to find good resources on it.