Detecting positive or negative sentiments in user reviews

In this section, we're going to look at detecting positive and negative sentiments in user reviews. In other words, we are going to detect whether the user is typing a positive comment or a negative comment about the product or service. We're going to use Word2Vec and Doc2Vec specifically and the gensim Python library for those services. There are two categories, which are positive and negative, and we have over 3,000 different reviews to look at. These come from Yelp, IMDb, and Amazon. Let's begin the code by importing the gensim library, which provides Word2Vec and Doc2Vec for logging to note status of the messages:

First, we will see how to load a pre-built Word2Vec model, provided by Google, that has been trained on billions of pages of text and has ultimately produced 300-dimensional vectors for all the different words. Once the model is loaded, we will look at the vector for cat. This shows that the model is a 300-dimensional...