Sentiment Analysis Web App (Backend)

Set up in local computer

• Clone the repo

  git clone https://github.com/dhrubomoy/sa-backend.git
  

• Install virtualenv if not installed

  pip install virtualenv
  

• Create a virtual environment

  cd sa-backend
  virtualenv env
  

• Activate the environment

  source env/bin/activate
  

• Install packages

  pip install -r requirements.txt
  

• Open the /src/sentiment_analysis/utils/twitter_analysis_utils.py file and replace following lines with your actual consumer key and access token for twitter api

  consumer_key = 'YOUR CONSUMER KEY'
  consumer_secret = 'YOUR CONSUMER SECRET'
  access_token = 'YOUR ACCESS TOKEN'
  access_token_secret = 'YOUR ACCESS TOKEN SECRET'
  

• Copy the models in src/files/models
  • Use existing model or tokenizer: Create a folder called models inside src/files/. Download these models and save it inside the models folder: rnn-glove-model-01-0.8362.hdf5 and rnn-word2vec-model-03-0.8425.hdf5
  • If you create a new model and tokenizer: Create a folder called models inside src/files/. After training the model, copy the saved model in models folder, save the tokenizer in src/files/tokenizers folder. Make sure that that the names of the files are reflected in src⁩/⁨sentiment_analysis⁩/⁨utils⁩/sentiment_analysis_utils⁩/rnn_model.py file by updating variables such as RNN_W2V_MODEL_PATH, RNN_GLOVE_MODEL_PATH etc.

Then download all the .hdf5 files from our google drive shared folder (mlsa-project/models) and save them inside the src/files/models/ folder that you created.

• Apply migrations

  cd src
  python manage.py migrate
  

• Run server

  python manage.py runserver
  

The server should be up and running. Check http://127.0.0.1:8000/api/searched_tweets/

Set up frontend in local computer

Follow instruction from sa-frontend repository README

Deploy to AWS (EC2)

Unfortunately deploying both backend and frontend to aws is quite tedious, and I couldn't find an easier way to make it easier. Follow these instructions:

1. Deploy backend
2. Deploy frontend

Once you've set up the server, everytime you stop and start the server, you must do step 6,8, and 9 mentioned in this file