food-reviews-sentiment-analysis

5-Stars Food Reviews Regressor Building a simple regressor that predicts the review star of given food text review, 80% accuracy was achieved and about 0.38 Mean-Squared Error (MSE). How it Works A neural network with Embedding layer as first layer, Long & Short Term Memory (LSTMs) since text is sequential data, then one fully connected neuron (dense) with linear activation function for regression ( continuous ratings ). The basic architecture is in the image below: Requirements ## Dataset Amazon Fine Food Reviews: large dataset (more than 500K reviews ) that consists of reviews of fine foods from amazon. Download and extract Reviews.csv to data folder (training will not work without) keras for creating, training and testing model. sklearn for utils like splitting data. tqdm for printing progress bars. pandas numpy Test In case you want to test directly. Output: Train There is already a trained model in results folder. However you can tune some parameters in config.py to improve MSE such as number of LSTM units, embedding size, etc. then run: