Predicting topics for customer reviews based on topics mapped to n-grams? I have a large number of unlabelled customer review data(text column) and my objective is to classify each review to a particular topic.


Also I have a list of unigrams,bigrams and trigrams(not a part of the customer review data) where each n-gram(word column) is tagged with a particular topic(topic column).



Is there a way to classify the customer reviews to the topics associated with the n-grams? 
I am mostly thinking in terms of Word2Vec and Doc2Vec approaches.
You might want to look at the Facebook starspace library - their examples are similar but with suggesting Facebook pages and hashtags.

https://github.com/facebookresearch/StarSpace