from sklearn.feature_extraction.text import CountVectorizer

from sklearn.datasets import fetch_20newsgroups

from nltk.corpus import names

from nltk.stem import WordNetLemmatizer

from sklearn.decomposition import NMF

def letters_only(astr):

for c in astr:

if not c.isalpha():

return False

return True

cv = CountVectorizer(stop_words="english", max_features=500)

groups = fetch_20newsgroups()

cleaned = []

all_names = set(names.words())

lemmatizer = WordNetLemmatizer()

for post in groups.data:

cleaned.append(' '.join([lemmatizer.lemmatize(word.lower())

for word in post.split()

if letters_only(word)

and word not in all_names]))

transformed = cv.fit_transform(cleaned)

nmf = NMF(n_components=100, random_state=43).fit(transformed)

for topic_idx, topic in enumerate(nmf.components_):

label = '{}: '.format(topic_idx)

print(label, " ".join([cv.get_feature_names()[i]

for i in topic.argsort()[:-9:-1]]))