Making Predictions with Word2vec
In this recipe, we use the previously learned embedding strategies to perform classification.
Getting ready
Now that we have created and saved CBOW word embeddings, we need to use them to make sentiment predictions on the movie data set. In this recipe, we will learn how to load and use prior-trained embeddings and use these embeddings to perform sentiment analysis by training a logistic linear model to predict a good or bad review.
Sentiment analysis is a really hard task to do because human language makes it very hard to grasp the subtleties and nuances of the true meaning. Sarcasm, jokes, and ambiguous references all make the task exponentially harder. We will create a simple logistic regression on the movie review data set to see whether we can get any information out of the CBOW embeddings we created and saved in the prior recipe. Since the focus of this recipe is in the loading and usage of saved embeddings, we will not pursue more complicated models.
