from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

df['mixed_feature'] = df['Text'] +' '+ df['Topic']

X_train, X_test, y_train, y_test = train_test_split(df['mixed_feature'], df['Sentiment'], test_size=0.2)

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)
clf = MultinomialNB().fit(X_train_tfidf, y_train)

clf.predict(count_vect.transform([df['Text][i]]))  ## For whatever text is in row i