Performing sentiment analysis with embedding and deep learning
In this section, we will train a model for conducting sentiment analysis on movie reviews. The model we will train will be able to classify reviews as positive or negative. To build and train the model, we will exploit the elements we have encountered so far. In brief, we’re doing the following:
- We are preprocessing the dataset, transforming in numerical vectors, and harmonizing the vectors
- We are defining a neural network with an embedding and training it
The dataset consists of 50,000 positive and negative reviews. We can see that it contains a heterogeneous length for reviews and that on average, there are 230 words:
Figure 1.16 – Graphs showing the distribution of the length of the review in the text; the left plot is for positive reviews, while the right plot is for negative reviews
In addition, the most prevalent words are, obviously, “movie”...
