title: Word2Vec: Is it possible to train with respect to weight in NLP?
body: <p>I used <code>Gensim</code>'s <code>Word2Vec</code> for training most similar words.</p>
  
  <p>My dataset is all posts from my college community site.</p>
  
  <p>Each dataset consists of like this:</p>
  
  <pre><code>(title) + (contents) + (all comments)  // String
  </code></pre>
  
  <p>For example, </p>
  
  <pre><code>data[0] =&gt; "This is title. Contents is funny. What so funny?. Not funny for me"
  </code></pre>
  
  <p>So, I have around 400,000 datas like above and make them as a vector and try to train these data via <code>Word2Vec</code>. </p>
  
  <p>I wonder that whether it is possible to make <code>Word2Vec</code> consider WEIGHT, which means, if I give an weight to certain data vector, <code>Word2Vec</code> train this data in a way that each word in this data vector has more strong relationship(similarity).</p>
  
  <p>For example, If I gave a weight 5 to dataset, <code>I like Pizza, Chicken</code>, the word <code>Pizza</code> and <code>Chicken</code> (or <code>like</code> and <code>Pizza</code> etc) has strong relations than other data vector's words.</p>
  
  <p>Would that be possible?</p>
  
  <p>Sorry for poor explanation but I'm not native english speaker. If need more detailed info, please post comment.</p>