code: def create_reviews_data(test_size):
      """Create reviews dataset for sentiment analysis.
  
      param test_size: Size of the test dataset.
      type test_size: float
      return: Tuple of X_train, X_test, y_train, y_test.
      rtype: Tuple of list, list, list, list
      """
      reviews_data = retrieve_dataset("reviews.json")
      papers = reviews_data["paper"]
      reviews = []
      evaluation = []
      for paper in papers:
          if paper["review"] is None or not paper["review"]:
              continue
          reviews.append(paper["review"][0]["text"])
          evaluation.append(paper["review"][0]["evaluation"])
      return train_test_split(
          reviews, evaluation, test_size=test_size, random_state=7
      )
  
file_path: responsible-ai-toolbox/rai_test_utils/rai_test_utils/datasets/tabular/classification_data_utils.py
imports: import numpy
  import pandas
  import shap
  from pandas import read_csv
  from sklearn.datasets import load_breast_cancer, load_iris, load_wine, make_classification
  from sklearn.model_selection import train_test_split
  from rai_test_utils.utilities import retrieve_dataset
label_desc: Use image node_id 10 for calling a global function with example usage: create_reviews_data(test_size) and returns: train_test_split