This is a simple proof of concept of how we can apply basic nltk techniques to perform sentiment analysis of The Message (MSG) translation of the bible leveraging tensorflow.

High level approach:

• Create an array of the most frequntly occuring (lemmatized) words from the training samples
• Create feature/label sets for positive and negative sentiment data by counting the number of popular words in each sample, from the array created above
• Using the above labelled features as inputs, train a 3 layer feedfoward neural network which will output an array containing probability percentages for True and False
• We save the model for later use and later run it on the Bible (MSG) saving the results in a sqlite database

Note, the basis for this comes largely from Rachit Mishra's work. I amended with the below:

• added a method to create the layers of the neural network
• saved the model after training for later re-use
• logged training results to be able to view in tensorboard
• ran the model on every verse from 3 books of the bible
  • Ecclesiastes
  • Proverbs
  • Psalms
• saved the results of the above predictions to a sqlite db

Also note that the training data comes from a movie review corpus so the accuracy of results against one of the oldest texts in history are questionable at best, even though I chose to use a modern translation of the bible (MSG)