Product Review Classification

An RNN-based model (and two variants, LSTM & BiLSTM) to classify a dataset of Persian product reviews (Digikala). Existing classes are: Recommended, Not Recommended, and Neutral.

Instructions:

• First do a $ pip install -r requirements.txt to install the required modules.
• To run the project from system's terminal, you might need to activate virtual environment of this project:
  $ source bin/bin/activate. You might find it easier to run it on Colab.
• Training: To train a model, run $ python main.py train model. For example, to train the BiLSTM model run $ python main.py train bilstm.
• Testing: To test a model with provided dataset, run $ python main.py test model. Similar to the train command, run $ python main.py test bilstm to test the trained BiLSTM model.
• Available models are: RNN, LSTM, and BiLSTM. Input to the command-line is automatically converted to lowercase.

Project Structure:

• A small dataset for testing purposes is located in the data directory. You have to acquire the training dataset/permission from Digikala. Then you can email me and I will send you the training data that I used for this project.
• Config.json can be used to change parameters of models, default options, training iterations, etc.