I'm studying sentimental analysis with python library nltk, following this example:
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)
I'm wondering how to classify a new review after having trained the classifier. I mean, if I had "Delicious!!", how to put it in the classifier to have 0 or 1 as result?