Using t-SNE to visualize similar words from texts
First of all install dependencies
$ pip install gensim numpy matplotlib sklearn
We want to visualize the classification cluster from a data set, in our case it’s a text, and we expect to classify the words in the text to clusters with same kind of information. Think of that, we can search for computer and we want to classify the words with same meaning, like laptop, laptop-computer, pc, even Dell.
For do that we can use t-Distributed Stochastic Neighbor Embedding (t-SNE). t-SNE technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets.
import gensim
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
The data can be downloaded from command prompt:
$ wget -c "https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz"
Now we can train the our model directly using gensim a python module with support to word2vec.
model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary=True)
# Test the loaded word2vec model in gensim
# We will need the raw vector for a word
print(model['computer'])
# We will also need to get the words closest to a word
model.similar_by_word('computer')
[ 1.07421875e-01 -2.01171875e-01 1.23046875e-01 2.11914062e-01
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3.23486328e-03 -4.80957031e-02 -2.48046875e-01 -6.20117188e-02]
[('computers', 0.79793781042099),
('laptop', 0.6640492081642151),
('laptop_computer', 0.6548866629600525),
('Computer', 0.6473335027694702),
('com_puter', 0.6082081198692322),
('technician_Leonard_Luchko', 0.5662747025489807),
('mainframes_minicomputers', 0.5617724061012268),
('laptop_computers', 0.5585451126098633),
('PC', 0.5539618134498596),
('maker_Dell_DELL.O', 0.5519253611564636)]
def display_closestwords_tsnescatterplot(model, word):
arr = np.empty((0,300), dtype='f')
word_labels = [word]
# get close words
close_words = model.similar_by_word(word)
# add the vector for each of the closest words to the array
arr = np.append(arr, np.array([model[word]]), axis=0)
for wrd_score in close_words:
wrd_vector = model[wrd_score[0]]
word_labels.append(wrd_score[0])
arr = np.append(arr, np.array([wrd_vector]), axis=0)
# find tsne coords for 2 dimensions
tsne = TSNE(n_components=2, random_state=0)
np.set_printoptions(suppress=True)
Y = tsne.fit_transform(arr)
x_coords = Y[:, 0]
y_coords = Y[:, 1]
# display scatter plot
plt.scatter(x_coords, y_coords)
for label, x, y in zip(word_labels, x_coords, y_coords):
plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')
plt.xlim(x_coords.min()+0.00005, x_coords.max()+0.00005)
plt.ylim(y_coords.min()+0.00005, y_coords.max()+0.00005)
plt.show()
display_closestwords_tsnescatterplot(model, 'life')
