title: best training methods for binary text classification using doc2vec gensim
body: <p>I am trying to use doc2vec to do text classification based on document subject, for example, I want to classify all documents about sports as 1 and all other documents as 0. I want to do this by first training a doc2vec model with training data and then use a classification model such as logistic regression to classify the texts as positive or negative.</p>
  
  <p>I have seen various examples online to do this [<a href="https://fzr72725.github.io/2018/01/14/genism-guide.html" rel="nofollow noreferrer">1</a>,<a href="https://towardsdatascience.com/multi-class-text-classification-with-doc2vec-logistic-regression-9da9947b43f4" rel="nofollow noreferrer">2</a>] which employ different methods and I am unclear about some of the details as to why they are using certain methods, and which method is the best for text classification.</p>
  
  <ol>
  <li><p>Firstly Using the example above, is it better to train the model using just documents related to sports or documents on all subjects. My thinking was by training just on sports documents you could classify documents based on document similarity(although this wouldnt produce vectors for non sports documents to use to train the next model). Also, i feel like if training the model on all documents you would need a huge amount of documents to represent everything other than sports to get good classification. </p></li>
  <li><p>Secondly, which features are actually used to train the logistic regression model. If training the model on all documents I assume you would track the documents using an index of some sort and then train the logistic regression model using the vectors with a class label, is this correct ? </p></li>
  <li><p>Thirdly, I have seen various uses of TaggedDocument where a unique id is put for each document and also where a shared id is used to represent the same class, eg., 1 = sports 0 = non sports. From what I have read a shared id means the model has a single vector representing each class, while using a unique id provides unique vectors for each document, is this correct ?. If so, assuming that I need unique labeled vectors for training the logistic regression model what is the point of using a shared id ? Wouldnt this provide terrible classification results ?</p></li>
  </ol>
  
  <p>If anyone can help me with the questions above and generally what is the best way to do text classification using doc2vec vectors it would be greatly appreciated.</p>