from sklearn.feature_extraction.text import CountVectorizer

keys_1 = ['funny', 'amusing', 'humorous', 'hilarious', 'jolly']
keys_2 = ['horror', 'fear', 'shock', 'panic', 'scream']
keys_3 = ['romantic', 'intimate', 'passionate', 'love', 'fond']

text = ('funny amusing fear passionate')

for i in range(3):
    keys = 'keys_' + str(i+1)
    cv = CountVectorizer(vocabulary = keys)
    data = cv.fit_transform([text]).toarray()
    print(data)

cv1 = CountVectorizer(vocabulary = keys_1)
data = cv1.fit_transform([text]).toarray()
print(data)

cv2 = CountVectorizer(vocabulary = keys_2)
data = cv2.fit_transform([text]).toarray()
print(data)

cv3 = CountVectorizer(vocabulary = keys_3)
data = cv3.fit_transform([text]).toarray()
print(data)