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 __init__(self, review_file, run_balance=True):
      self.run_balance = run_balance
  
      print("Parsing and processing json file")
      data = []
  
      with open(review_file, "r") as f:
          for line in f:
              data_line = json.loads(line)
              selected_row = []
              for item in good_columns:
                  selected_row.append(data_line[item])
              # as we read in, clean
              data.append(review_to_sentiment(selected_row))
  
      # Not sure how to easily balance outside of pandas...but should replace eventually
      self.amazon = pd.DataFrame(
          data, columns=["Sentiment", "clean_text"]
      )
      self.all_text = self.amazon["clean_text"]
      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 1 to create a new Amazon_Reviews object from inherited base classes: object with example: obj = Amazon_Reviews(review_file, run_balance)