title: Better text documents clustering than tf/idf and cosine similarity?
body: <p>I'm trying to cluster the Twitter stream. I want to put each tweet to a cluster that talk about the same topic. I tried to cluster the stream using an online clustering algorithm with tf/idf and cosine similarity but I found that the results are quite bad.</p>
  
  <p>The main disadvantages of using tf/idf is that it clusters documents that are keyword similar so it's only good to identify near identical documents. For example consider the following sentences:</p>
  
  <p>1- The website Stackoverflow is a nice place.
  2- Stackoverflow is a website.</p>
  
  <p>The prevoiuse two sentences will likely by clustered together with a reasonable threshold value since they share a lot of keywords. But now consider the following two sentences:</p>
  
  <p>1- The website Stackoverflow is a nice place.
  2- I visit Stackoverflow regularly.</p>
  
  <p>Now by using tf/idf the clustering algorithm will fail miserably because they only share one keyword even tho they both talk about the same topic.</p>
  
  <p>My question: is there better techniques to cluster documents?</p>