How to approach TF-IDf based analysis? Problem statement :

We have documents with list of words in them.
Overall these documents are classified into 2 group (say, good quality vs bad)

docs -

doc1 = [w1,w2,w3,w4]
doc2 = [w4,w3,w3,w4]
doc3 = [w2,w4,w8,w1]
doc4 = [w5,w4,w0,w9]


doc group -

good_grp = [doc2, doc1]
bad_grp = [doc3, doc4]


Now we have to find out which words actually are important to make the document good vs bad ?

Idea 1: 
Merge all words from documents that belong to document group 1 into single document say (good quality doc) and other one being (bad quality doc) and calculate tf-idf score per doc; but in this case we lose information of document level words and now just see document group level word importance.

doc1 = [w1,w2,w3,w4]
doc2 = [w4,w3,w3,w4]
doc3 = [w2,w4,w8,w1]
doc4 = [w5,w4,w0,w9]

good_grp = [w1,w2,w3,w4,w4,w3,w3,w4]
bad_grp = [w2,w4,w8,w1,w5,w4,w0,w9]


Can someone help me to direct to a better approach tf-idf or any other technique to solve this problem?
I think here you must maintain the actual tf-idf and create corpus over it.. Assuming you already have lables for documents available. You can rum classification over it. 

Best classification I am anticipating for this problem would be naive bayes..