title: Am I using word-embeddings correctly?
body: <p><strong>Core question : Right way(s) of using word-embeddings to represent text ?</strong> </p>
  
  <p>I am building sentiment classification application for tweets. Classify tweets as - negative, neutral and positive.
  I am doing this using Keras on top of theano and using word-embeddings (google's word2vec or Stanfords GloVe).</p>
  
  <p>To represent tweet text I have done as follows:</p>
  
  <ol>
  <li>used a pre-trained model (such as word2vec-twitter model) [<strong>M</strong>] to map words to their embeddings.</li>
  <li>Use the words in the text to query <strong>M</strong> to get corresponding vectors. So if the tweet (<strong>T</strong>) is "Hello world" and <strong>M</strong> gives vectors <strong>V1</strong> and <strong>V2</strong> for the words 'Hello' and 'World'.</li>
  <li>The tweet <strong>T</strong> can then be represented (<strong>V</strong>) as either <strong>V1</strong>+<strong>V2</strong> (add vectors) or <strong>V1V2</strong> (concatinate vectors)[These are 2 different strategies] [Concatenation means juxtaposition, so if <strong>V1, V2</strong> are d-dimension vectors, in my example <strong>T</strong> is 2d dimension vector]</li>
  <li>Then, the tweet <strong>T</strong> is represented by vector <strong>V</strong>.</li>
  </ol>
  
  <p>If I follow the above, then My Dataset is nothing but vectors (which are sum or concatenation of word vectors depending on which strategy I use). 
  I am training a deepnet such as FFN, LSTM on this dataset. But my results arent coming out to be great.</p>
  
  <p>Is this the right way to use word-embeddings to represent text ? What are the other better ways ?</p>
  
  <p>Your feedback/critique will be of immense help.</p>