Tensorflow/Keras neural network to train on the IMDB dataset of 50,000 movie reviews and classify reviews as positive or negative using binary sentiment classification with 87% accuracy.
The dataset is a collection of reviews (25,000 for training and 25,000 for testing), each one being a movie review as an array of "words", each word represented as an integer which maps to a word in the word index. The word index is a dictionary of nearly a hundred thousand words.
Each review has an associated label, which is a binary integer representing whether the review is positive or negative.
Adapted/fixed/modified/annotated starting from a tutorial by @TechWithTimm.
Python version: 3.7.4
See dependencies.txt for packages and versions (and below to install).
Each review input, after preprocessing, is an array of "words", represented as integers that map to a word in the word index, truncated/padded as necessary to 250 words.
Embedding and GlobalAveragePooling1D layers: Groups similar words in the word index together, based on the context that they are used in.
Hidden layer: 16 neurons.
Output layer: 1 neuron with a value between 0 and 1 (squashed using the sigmoid function) denoting whether the review is positive or negative.
For more details of the model's architecture, refer to the comment annotations in the code.
Clone the Git repo.
Install the dependencies:
pip install -r dependencies.txtpython main.py