title: Using LDA(topic model) : the distrubution of each topic over words are similar and "flat"
body: <p>Latent Dirichlet Allocation(LDA) is a topic model to find latent variable (topics) underlying a bunch of documents. I'm using python gensim package and having two problems: </p>
  
  <ol>
  <li><p>I printed out the most frequent words for each topic (I tried 10,20,50 topics), and found out that the distribution over words is very "flat": meaning even the most frequent word has only 1% probability... </p></li>
  <li><p>Most of the topics are similar: meaning the most frequent words for each of the topics overlap a lot and the topics share almost the same set of words for their high frequency words...</p></li>
  </ol>
  
  <p>I guess the problem is probably due to my documents: my documents actually belong to a specific category, for example, they are all documents introducing different online games. For my case, will LDA still work, since the documents themselves are quite similar, so a model based on "bag of words" may not be a good way to try? </p>
  
  <p>Could anyone give me some suggestions? Thank you!</p>