code: def review_to_sentiment(review):
      # Review is coming in as overall (the rating, reviewText, and summary)
      # this then cleans the summary and review and gives it a positive or negative value
      norm_text = normalize(review[2] + " " + review[1])
      review_sent = ["neutral", norm_text]
      if review[0] > 3:
          review_sent = ["positive", norm_text]
      elif review[0] < 3:
          review_sent = ["negative", norm_text]
  
      return review_sent
  
file_path: nlp-architect/examples/supervised_sentiment/amazon_reviews.py
imports: import json
  import pandas
  from nlp_architect.utils.generic import normalize, balance
label_desc: Use image node_id 1 for calling a global function with example usage: review_to_sentiment(review) and returns: review_sent