class_docstr: Take the *.json file of Amazon reviews as downloaded from
  http://jmcauley.ucsd.edu/data/amazon/
  Then does data cleaning and balancing, as well as transforms the reviews 1-5 to a sentiment
code: def process(self):
      self.amazon = self.amazon[
          self.amazon["Sentiment"].isin(["positive", "negative"])
      ]
  
      if self.run_balance:
          # balance it out
          self.amazon = balance(self.amazon)
  
      print("Sample Data")
      print(self.amazon[["Sentiment", "clean_text"]].head())
  
      # mapping of the labels with dummies (has headers)
      self.labels_0 = pd.get_dummies(self.amazon["Sentiment"])
      self.labels = self.labels_0.values
      self.text = self.amazon["clean_text"].values
  
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 2 for calling the Amazon_Reviews obj's underlying member method code with example usage: obj.process() without return types