  This paper describes a technique to compare large text sources using word
vector representations (word2vec) and dimensionality reduction (t-SNE) and how
it can be implemented using Python. The technique provides a bird's-eye view of
text sources, e.g. text summaries and their source material, and enables users
to explore text sources like a geographical map. Word vector representations
capture many linguistic properties such as gender, tense, plurality and even
semantic concepts like "capital city of". Using dimensionality reduction, a 2D
map can be computed where semantically similar words are close to each other.
The technique uses the word2vec model from the gensim Python library and t-SNE
from scikit-learn.
