content: # Natural Language Processing
  
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)
  
  # Cleaning the texts
  import re
  import nltk
  nltk.download('stopwords')
  from nltk.corpus import stopwords
  from nltk.stem.porter import PorterStemmer
  corpus = []
  for i in range(0, 1000):
      review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
      review = review.lower()
      review = review.split()
      ps = PorterStemmer()
      review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
      review = ' '.join(review)
      corpus.append(review)
  
  # Creating the Bag of Words model
  from sklearn.feature_extraction.text import CountVectorizer
  cv = CountVectorizer(max_features = 1500)
  X = cv.fit_transform(corpus).toarray()
  y = dataset.iloc[:, 1].values
  
  # Splitting the dataset into the Training set and Test set
  from sklearn.cross_validation import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
  
  # Fitting Naive Bayes to the Training set
  from sklearn.naive_bayes import GaussianNB
  classifier = GaussianNB()
  classifier.fit(X_train, y_train)
  
  # Predicting the Test set results
  y_pred = classifier.predict(X_test)
  
  # Making the Confusion Matrix
  from sklearn.metrics import confusion_matrix
  cm = confusion_matrix(y_test, y_pred)