World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITians, only for AI Learners.
Designed by IITians, only for AI Learners.
New to InsideAIML? Create an account
Employer? Create an account
Download our e-book of Introduction To Python
4.5 (1,292 Ratings)
559 Learners
Ajinkya Gandhi
3 years ago
# Lets See how the movies are classified
from nltk.corpus import movie_reviews
all_cats = []
for w in movie_reviews.categories():
all_cats.append(w.lower())
print(all_cats)
['neg', 'pos']
from nltk.corpus import movie_reviews
from nltk.tokenize import sent_tokenize
fields = movie_reviews.fileids()
sample = movie_reviews.raw("pos/cv944_13521.txt")
token = sent_tokenize(sample)
for lines in range(4):
print(token[lines])
meteor threat set to blow away all volcanoes & twisters !
summer is here again !
this season could probably be the most ambitious = season this decade with hollywood churning out films
like deep impact , = godzilla , the x-files , armageddon , the truman show ,
all of which has but = one main aim , to rock the box office .
leading the pack this summer is = deep impact , one of the first few film
releases from the = spielberg-katzenberg-geffen's dreamworks production company .
import nltk
from nltk.corpus import movie_reviews
fields = movie_reviews.fileids()
all_words = []
for w in movie_reviews.words():
all_words.append(w.lower())
all_words = nltk.FreqDist(all_words)
print(all_words.most_common(10))
[(,', 77717), (the', 76529), (.', 65876), (a', 38106), (and', 35576),
(of', 34123), (to', 31937), (u"'", 30585), (is', 25195), (in', 21822)]