This code belongs to the "Build and Visualize Word2Vec Model on Amazon Reviews" blog post.
Word2vec is a very popular Natural Language Processing technique nowadays that uses a neural network to learn the vector representations of words called "word embeddings" in a particular text.
In this tutorial, we will use the excellent implementation of word2vec from the gensim package to build our word2vec model. We will use t-Distributed Stochastic Neighbor Embedding (t-SNE) in sklearn to visualize the learned embeddings vectors.
- Python 2.7
- Jupyter Notebook
- gensim
- numpy
- pandas
- natural language toolkit (nltk)
We will use the Amazon review corpus on Health and Personal Care. The dataset is in json format and contains 346,355 reviews.
we visualize the learned embeddings using t-SNE. t-SNE is a tool for data visualization that reduces the dimensionality of data to 2 or 3 dimensions so that it can be plotted easily.
One way to check if we have a good word2vec model is to use the model to find the most similar words to a specific word. For that, we can use the most_similar function that returns the 10 most similar words to the given word. Let's find the most similar words to the word blue.
