Naive Bayes and Support Vector Machine (NBSVM) Classification
you use sklearn "CountVectorizer" and "TfidfVectorizer" to covert the text data into vector

    tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')
X_train, X_test, y_train, y_test = train_test_split(df['text'], df['class'], random_state = 0)

# vector representations of the text 
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)

# Building a SVM model
svmmodel = LinearSVC().fit(X_train_tfidf, y_train)