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- dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = 't', quoting = 3)
- 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)
- cv = CountVectorizer(max_features = 1500)
- X = cv.fit_transform(corpus).toarray()
- y = dataset.iloc[:, 1].values
- X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
- classifier = GaussianNB()
- classifier.fit(X_train, y_train)
- y_pred = classifier.predict(X_test)
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