The folks at Knoyd put together a word embedding example by scraping a Python Facebook group:
We are going to represent the content of a Facebook post using word embeddings and comparing the transformed posts using word mover’s distance. The combination of both have shown lower k-nearest neighbor-document classification error rates compared to other state of the art techniques.
The advantage of word embeddings is that the words which have similar meanings but don’t have any letters in common will still have similar vectors (be close) in the embedded space (e.g. lion and tiger).
There’s a good high-level discussion of techniques in this post.