Word embedding represents the density of the word vector, unlike what we have done with the Countvectorizer. It is a different way to preprocess the data. This embedding can map semantically similar words. It does not consider the text as a human language but maps the structure of sets of words used in the corpus. They aim to map words into a geometric space which is called an embedding space.