title: sentiment analysis to find top 3 adjectives for products in tweets
body: <p>there is a sentiment analysis tool to find out people's perception on social network.
  This tool can:
  (1) Decompose a document into a set of sentences.</p>
  
  <p>(2) Decompose each sentence into a set of words, and perform filtering such that only
  product name and adjectives are preserved.</p>
  
  <p>e.g. "This MacBook is awesome. Sony is better than Macbook."</p>
  
  <p>After processing, We can get:</p>
  
  <p>{MacBook, awesome} </p>
  
  <p>{Sony, better}. (not the truth :D)</p>
  
  <p>We just assume there exists a list of product names, P, that we will ever
  care, and there exist a list of adjectives, A, that we will ever care.</p>
  
  <p>My questions are:</p>
  
  <ol>
  <li><p>Can we reduce this problem into a specialized association rule mining
  problem and how? If yes, anything need to be noticed like reduction, parameter
  settings (minsup and minconf), additional constraints, and modication to the
  Aprior algorithm to solve the problem.</p></li>
  <li><p>Any way to artificially spam the result like adding "horrible" to the top 1 adjective? And any good ways to prevent this spam?</p></li>
  </ol>
  
  <p>Thanks.</p>