Word2Vec is dope. In short, it takes in a corpus, and churns out vectors for each of those words. What's so special about these vectors you ask? Well, similar words are near each other. Furthermore, these vectors represent how we use the words. For example, v_man - v_woman is approximately equal to v_king - v_queen, illustrating the relationship that "man is to woman as king is to queen". This process, in NLP voodoo, is called word embedding. These representations have been applied widely. This is made even more awesome with the introduction of Doc2Vec that represents not only words, but entire sentences and documents. Imagine being able to represent an entire sentence using a fixed-length vector and proceeding to run all your standard classification algorithms. Isn't that amazing?
However, Word2Vec documentation is shit. The C-code is nigh unreadable (700 lines of highly optimized, and sometimes weirdly optimized code). I personally spent a lot of time untangling Doc2Vec and crashing into ~50% accuracies due to implementation mistakes. This tutorial aims to help other users get off the ground using Word2Vec for their own research. We use Word2Vec for sentiment analysis by attempting to classify the Cornell IMDB movie review corpus (http://www.cs.cornell.edu/people/pabo/movie-review-data/). The specific data set used is available for download at http://ai.stanford.edu/~amaas/data/sentiment/.
The IPython Notebook (code + tutorial) can be found in word2vec-sentiments.ipynb
The code to just run the Doc2Vec and save the model as imdb.d2v can be found in run.py. Should be useful for running on computer clusters.
test-neg.txttest-pos.txttrain-neg.txttrain-pos.txttrain-unsup.txtTraining and testing data. Explained in more detail in the notebook.word2vec-sentiment.ipynbThe notebook (code + tutorial)run.pyJust the code
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