supervised tag suggestion for documents
I'm currently working on something similar,
besides what @Joonatan Samuel suggested I would encourage you to do careful preprocessing and considerations.

If you want two or more tags for documents you could train several model : one model per tag. You need to consider if there will be enough cases for each model (tag)
If you have a lot of tags, you could run into a problem with document-tag cases like above.
Stick to most common tag prediction don't try to predict all tags.
I have thousands of documents with associated tag information. However i also have many documents without tags.
I want to train a model on the documents WITH tags and then apply the trained classifier to the UNTAGGED documents; the classifier will then suggest the most appropriate tags for each UNTAGGED document.
I have done quite a lot of research and there doesn't seem to be a SUPERVISED implementation to document tag classification.
I know NLTK, gensim, word2vec and other libraries will be useful for this problem.
I will be coding the project in Python.
Any help would be greatly appreciated.