Chris Moody continues his series on natural language processing:
Counting and tensor decompositions are elegant and straightforward techniques. But these methods are grossly underepresented in business contexts. In this post we factorized an example made up of word skipgrams occurring within documents to arrive at word and document vectors simultaneously. This kind of analysis is effective, simple, and yields powerful concepts.
Look to your own data, and before throwing black-box deep learning machines at them, try out tensor factorizations!
He has a set of animated GIFs to help with learning, though I do wish they were about 30% slower so you can take a moment to read each section before it jumps to the next bit.