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)