Summary

We explored in this chapter one of the major innovation in text analysis, word embeddings or word vectors. Word vectors are unique in being not only a way for us to represent our documents and our words but to also offer a new way of looking at our words. The success of Word2Vec led to an explosion in various word embedding methods, each with its own quirks, advantages, and disadvantages. We not only learned about the popular Word2Vec and Doc2Vec implementations but also five other word embedding methods – all of them are supported well in the Gensim eco-system making them easy to use.