Reviewator

Project information

This project focuses on binary sentiment classification of movie reviews from the IMDb Reviews dataset, using three distinct models: a multilayer perceptron, a Word2Vec-based model and a recurrent neural network (RNN). Each model is designed to classify reviews as positive or negative, leveraging advanced techniques in natural language processing. The word embeddings generated by each model are visualized using dimensionality reduction techniques (PCA, t-SNE), providing an intuitive representation of the semantic space.

Models are assessed using confusion matrices to analyze classification accuracy and ROC curves to evaluate the trade-off between true positive and false positive rates. The project compares the performance of MLP, Word2Vec, and RNN in capturing sentiment from text data.

Python TensorFlow