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Using Word2Vec in Python

Word2Vec is a popular technique for natural language processing (NLP) that allows you to represent words as vectors in a high-dimensional space. This technique is useful for tasks such as text classification, sentiment analysis, and topic modeling. In this tutorial, we will show you how to use Word2Vec in Python using the Gensim library.

Installing the Required Libraries

Before we can start using Word2Vec, we need to install the required libraries. You can install the Gensim library using pip:


pip install gensim

Loading the Data

For this example, we will use a sample dataset of text documents. You can replace this with your own dataset.


from gensim.summarization.keypoints import keywords
from gensim.models import Word2Vec
from gensim.utils import tokenize
import numpy as np

# Sample dataset
sentences = [
    "The quick brown fox jumps over the lazy dog",
    "The sun is shining brightly in the clear blue sky",
    "The cat purrs contentedly on my lap",
    "The dog wags its tail with excitement",
    "The baby laughs at the silly clown"
]

Tokenizing the Data

Before we can train the Word2Vec model, we need to tokenize the data. Tokenization is the process of breaking down text into individual words or tokens.


# Tokenize the data
tokenized_sentences = [tokenize(sentence) for sentence in sentences]

Training the Word2Vec Model

Now we can train the Word2Vec model using the tokenized data.


# Train the Word2Vec model
model = Word2Vec(tokenized_sentences, vector_size=100, window=5, min_count=1)

Using the Word2Vec Model

Once the model is trained, we can use it to get the vector representation of a word.


# Get the vector representation of a word
vector = model.wv["dog"]
print(vector)

Finding Similar Words

We can also use the Word2Vec model to find similar words.


# Find similar words
similar_words = model.wv.most_similar("dog")
print(similar_words)

Visualizing the Word Vectors

We can use a library such as Matplotlib to visualize the word vectors.


import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

# Get the word vectors
word_vectors = model.wv.vectors

# Reduce the dimensionality of the word vectors using PCA
pca = PCA(n_components=2)
word_vectors_2d = pca.fit_transform(word_vectors)

# Plot the word vectors
plt.scatter(word_vectors_2d[:, 0], word_vectors_2d[:, 1])
for i, word in enumerate(model.wv.index_to_key):
    plt.annotate(word, (word_vectors_2d[i, 0], word_vectors_2d[i, 1]))
plt.show()

This is a basic example of how to use Word2Vec in Python. You can experiment with different parameters and techniques to improve the performance of the model.

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