I used a Python t-SNE library to reduce the 200 feature dimensions for each word to 2 dimensions and plotted them in matplotlib. I saved out the x/y coordinates for each word in the book, so that I can show those words on the graph as you mouse over the replaced (blue) words.
from gensim.scripts.glove2word2vec import glove2word2vec
glove2word2vec(glove_input_file=file, word2vec_output_file="gensim_glove_vectors.txt")
from gensim.models.keyedvectors import KeyedVectors
model = KeyedVectors.load_word2vec_format("gensim_glove_vectors.txt", binary=False)
If you saved your model with save(), you must use load()
load_word2vec_format is for the model generated by google, not for the model generated by gensim
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