In this section, we are going to build a machine learning model that can detect review sentiment (detect whether a review is positive or negative) using PyTorch. As a training set, we are going to use the Large Movie Review Dataset, which contains a set of 25,000 movie reviews for training and 25,000 for testing, both of which are highly polarized.
First, we have to develop parser and data loader classes to move the dataset to memory in a format suitable for use with PyTorch.
Let's start with the parser. The dataset we have is organized as follows: there are two folders for the train and test sets, and each of these folders contains two child folders named pos and neg, which is where the positive review files and negative review files are placed. Each file in the dataset contains exactly one review, and its sentiment is determined by...
