title: Do documents without labels add information to Facebook's FastText supervised classifier?
body: <p>I hope you guys are doing great.</p>
  <p>I'm training a classifier with Facebook's FastText to determine if a piece of text (tweet) is talking about economy or not.
  For doing this task, I have about 2200 tagged tweets as &quot;economy&quot; or &quot;not_economy&quot;, <strong>but I also have almost a million unlabeled tweets</strong>.</p>
  <p>Reading FastText's documentation I know the supervised input file should be a document with a tweet per line with a prefix of the shape <code>__label__economy</code> or <code>__label__not_economy</code>.</p>
  <p>The documentation doesn't talk about adding unlabeled documents to the unsupervised input file, but since it's a word embedding model, it's supposed to take context information from the word's text distribution, so I think giving the model all this extra information should help getting a better embedding representation of my vocabulary. For this reason I'm training the model (with <code>fasttext supervised -input tweets_input -output tweets_model</code>) but I'm also adding untagged documents at the end. The things is that all these almost 1M tweets doesn't seem to be enhancing the model at all.</p>
  <p>The other way I know I can take advantage of this data is training a unsupervised model and start using the sentence embedings to train a classifier.</p>
  <p>The question is the one in the title:</p>
  <p>Do documents without labels add information to Facebook's FastText supervised classifier? Is it better to get the document embeddings and train my own classifier with other library?</p>
  <p>Thanks for any information that helps me understand better.</p>