AmazonReview-Sentiment-Analysis

Sentiment Analysis using simple LSTM and Conv1D layers.

This is model that is used to get the sentiment rating(0-1 range). 0 means bad/worse and 1 means good sentiment.

Dependencies

• Python 3+
• Keras with tensorflow backend
• nvidia gpu (for training purpose as it use CuDNNLSTM layer that is accelerated by CuDNN library by nvidia)
• Numpy

How to use

1. Fork this repo
2. Download the dataset from here .
3. Download the GloVe Word embeddings from here.
4. Save both data and GloVe embeddings in data folder.
5. If training, make changes in file utils/config.py if you want. Change TRAINING_DATASET_SIZE accordingly to change the number of training sentences.
6. Use the train.ipynb notebook for training.
7. If using for test-predictions, download the weights from here and save it in weights folder.
8. Use inference.ipynb notebook.