def test_review(set_up):
imdb = Imdb(locale='en_US', cache=False)
reviews_data = imdb._get_reviews_data('tt0111161')
review = Review(data=reviews_data[0])
assert 'carflo' == review.username
assert review.text.startswith('Why do I want to write the 234th ') is True
assert review.text.endswith('Redemption to touch the soul.') is True
assert '2003-11-26' == review.date
assert 10 == review.rating
assert 'Tied for the best movie I have ever seen' == review.summary
assert 'G' == review.status
assert 'Texas' == review.user_location
assert 1902 <= review.user_score
assert 2207 <= review.user_score_count
from imdbpie import Imdb
imdb = Imdb()
for i in range(10):
filename = 'review_{}.txt'.format(i)
file = open(filename, 'w', encoding='utf8')
file.write(imdb._get_reviews_data('tt1457767')[i]['text'])
file.close()
file_title = 'review_combined.txt'
file = open(file_title, 'w', encoding='utf8')
for i in range(21):
file.write(imdb._get_reviews_data('tt1457767')[i]['text'])
file.write('\n\n\n')
file.close()
from imdbpie import Imdb
imdb = Imdb()
#for Stranger Things
#takes the first 10 reviews on imdb and prints them into their separate files
for i in range(10):
filename = 'review_{}.txt'.format(i)
file = open(filename, 'w', encoding='utf8')
file.write(imdb._get_reviews_data('tt4574334')[i]['text'])
file.close()
#takes all the separate reviews and combines them into 1 composite file called strangerthings_reviews
filenames = [
'review_0.txt', 'review_1.txt', 'review_2.txt', 'review_3.txt',
'review_4.txt', 'review_5.txt', 'review_6.txt', 'review_7.txt',
'review_8.txt', 'review_9.txt'
]
with open('strangerthings_reviews.txt', 'w') as outfile:
for fname in filenames:
with open(fname) as infile:
content = infile.read().replace('\n', '')
outfile.write(content)
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk
nltk.download('vader_lexicon')
sentence = open('strangerthings_reviews.txt', 'r', encoding='utf8').read()
score = SentimentIntensityAnalyzer().polarity_scores(sentence)
from imdbpie import Imdb
imdb = Imdb()
for i in range(10):
filename = 'review_{}.txt'.format(i)
file = open(filename, 'w', encoding='utf8')
file.write(imdb._get_reviews_data('tt1591095')[i]['text'])
file.close()
#takes all the separate reviews and combines them into 1 composite file called strangerthings_reviews
filenames = [
'review_0.txt', 'review_1.txt', 'review_2.txt', 'review_3.txt',
'review_4.txt', 'review_5.txt', 'review_6.txt', 'review_7.txt',
'review_8.txt', 'review_9.txt'
]
with open('insidious_reviews.txt', 'w') as outfile:
for fname in filenames:
with open(fname) as infile:
content = infile.read().replace('\n', '')
outfile.write(content)
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk
nltk.download('vader_lexicon')
sentence = open('insidious_reviews.txt', 'r', encoding='utf8').read()
score = SentimentIntensityAnalyzer().polarity_scores(sentence)
print(score)
print('The most used "significant" words & their freq. are:')
print('film: 18, soldiers: 5, stress: 4, soldier: 4')
print('story: 3, lives: 3, weapon: 2, violent: 2, trauma: 2')
print('training: 2, killing: 2, hard: 2, fighting: 2')
print('\n')
print('Words used pertaining to the emotional aspects of the movies:')
print('violent: 2, emotions: 2, emotional: 2, angry: 2')
#NLTK
#nltk.download() and click Models and download vador_lexicon
from imdbpie import Imdb
imdb = Imdb()
print(imdb.search_for_title("Lone Survivor")[0])
print(imdb._get_reviews_data("tt1091191")[0]['summary'])
print(imdb._get_reviews_data("tt1091191")[0]['user_name'])
print(imdb._get_reviews_data("tt1091191")[0]['date'])
print(imdb._get_reviews_data("tt1091191")[0]['text'])
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk
sentence = open('lone_survivor.txt', 'r', encoding='utf8').read()
score = SentimentIntensityAnalyzer().polarity_scores(sentence)
print(score)
