title: Manually Specifying a Topic Model in R
body: <p>I have a corpus of text <strong>with each line</strong> in the csv file <strong>uniquely specifying a "topic"</strong> I am interested in.  If I were to run an topic model on this corpus using an LDA or Gibbs method from either the topicmodels package or lda, as expected I would get multiple topics per "document" (a line of text in my CSV which I have a-priori defined to be my unique topic of interest).  I get that this is a result of the topic model's algorithm and the bag of words assumption.</p>
  
  <p>What I am curious about however is this</p>
  
  <p>1) Is there a pre-fab'd package in R that is designed for the user to specify the topics using the empirical word distribution? That is, I don't want the topics to be estimated; I want to tell R what the topics are.  I suppose I could run a topic model with the correct number of Topics, use that structure of the object and then overwrite its contents.  I was just hoping there was an easier or more obvious way that I'm just not seeing at this point.</p>
  
  <p>Thoughts?</p>
  
  <p>edit: added - 
  I just thought about the alpha and beta parameters having control over the topic/term distributions within the LDA modeling algorithm.  What settings might I be able to use that would force the model to only find 1 topic per document?  Or is there a setting which would allow for that to occur?</p>
  
  <p>If these seem like silly questions I understand - I'm quite new to this particular field and I am finding it fascinating.</p>