237 lines
12 KiB
Plaintext
237 lines
12 KiB
Plaintext
.. Copyright (C) 2001-2023 NLTK Project
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.. For license information, see LICENSE.TXT
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===================
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Sentiment Analysis
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===================
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>>> from nltk.classify import NaiveBayesClassifier
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>>> from nltk.corpus import subjectivity
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>>> from nltk.sentiment import SentimentAnalyzer
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>>> from nltk.sentiment.util import *
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>>> n_instances = 100
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>>> subj_docs = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')[:n_instances]]
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>>> obj_docs = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:n_instances]]
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>>> len(subj_docs), len(obj_docs)
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(100, 100)
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Each document is represented by a tuple (sentence, label). The sentence is tokenized,
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so it is represented by a list of strings:
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>>> subj_docs[0]
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(['smart', 'and', 'alert', ',', 'thirteen', 'conversations', 'about', 'one',
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'thing', 'is', 'a', 'small', 'gem', '.'], 'subj')
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We separately split subjective and objective instances to keep a balanced uniform
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class distribution in both train and test sets.
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>>> train_subj_docs = subj_docs[:80]
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>>> test_subj_docs = subj_docs[80:100]
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>>> train_obj_docs = obj_docs[:80]
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>>> test_obj_docs = obj_docs[80:100]
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>>> training_docs = train_subj_docs+train_obj_docs
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>>> testing_docs = test_subj_docs+test_obj_docs
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>>> sentim_analyzer = SentimentAnalyzer()
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>>> all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
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We use simple unigram word features, handling negation:
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>>> unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
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>>> len(unigram_feats)
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83
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>>> sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
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We apply features to obtain a feature-value representation of our datasets:
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>>> training_set = sentim_analyzer.apply_features(training_docs)
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>>> test_set = sentim_analyzer.apply_features(testing_docs)
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We can now train our classifier on the training set, and subsequently output the
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evaluation results:
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>>> trainer = NaiveBayesClassifier.train
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>>> classifier = sentim_analyzer.train(trainer, training_set)
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Training classifier
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>>> for key,value in sorted(sentim_analyzer.evaluate(test_set).items()):
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... print('{0}: {1}'.format(key, value))
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Evaluating NaiveBayesClassifier results...
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Accuracy: 0.8
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F-measure [obj]: 0.8
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F-measure [subj]: 0.8
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Precision [obj]: 0.8
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Precision [subj]: 0.8
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Recall [obj]: 0.8
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Recall [subj]: 0.8
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Vader
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------
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>>> from nltk.sentiment.vader import SentimentIntensityAnalyzer
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>>> sentences = ["VADER is smart, handsome, and funny.", # positive sentence example
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... "VADER is smart, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted)
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... "VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted)
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... "VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled
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... "VADER is VERY SMART, handsome, and FUNNY!!!",# combination of signals - VADER appropriately adjusts intensity
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... "VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!",# booster words & punctuation make this close to ceiling for score
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... "The book was good.", # positive sentence
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... "The book was kind of good.", # qualified positive sentence is handled correctly (intensity adjusted)
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... "The plot was good, but the characters are uncompelling and the dialog is not great.", # mixed negation sentence
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... "A really bad, horrible book.", # negative sentence with booster words
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... "At least it isn't a horrible book.", # negated negative sentence with contraction
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... ":) and :D", # emoticons handled
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... "", # an empty string is correctly handled
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... "Today sux", # negative slang handled
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... "Today sux!", # negative slang with punctuation emphasis handled
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... "Today SUX!", # negative slang with capitalization emphasis
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... "Today kinda sux! But I'll get by, lol" # mixed sentiment example with slang and constrastive conjunction "but"
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... ]
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>>> paragraph = "It was one of the worst movies I've seen, despite good reviews. \
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... Unbelievably bad acting!! Poor direction. VERY poor production. \
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... The movie was bad. Very bad movie. VERY bad movie. VERY BAD movie. VERY BAD movie!"
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>>> from nltk import tokenize
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>>> lines_list = tokenize.sent_tokenize(paragraph)
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>>> sentences.extend(lines_list)
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>>> tricky_sentences = [
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... "Most automated sentiment analysis tools are shit.",
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... "VADER sentiment analysis is the shit.",
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... "Sentiment analysis has never been good.",
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... "Sentiment analysis with VADER has never been this good.",
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... "Warren Beatty has never been so entertaining.",
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... "I won't say that the movie is astounding and I wouldn't claim that \
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... the movie is too banal either.",
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... "I like to hate Michael Bay films, but I couldn't fault this one",
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... "I like to hate Michael Bay films, BUT I couldn't help but fault this one",
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... "It's one thing to watch an Uwe Boll film, but another thing entirely \
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... to pay for it",
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... "The movie was too good",
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... "This movie was actually neither that funny, nor super witty.",
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... "This movie doesn't care about cleverness, wit or any other kind of \
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... intelligent humor.",
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... "Those who find ugly meanings in beautiful things are corrupt without \
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... being charming.",
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... "There are slow and repetitive parts, BUT it has just enough spice to \
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... keep it interesting.",
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... "The script is not fantastic, but the acting is decent and the cinematography \
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... is EXCELLENT!",
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... "Roger Dodger is one of the most compelling variations on this theme.",
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... "Roger Dodger is one of the least compelling variations on this theme.",
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... "Roger Dodger is at least compelling as a variation on the theme.",
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... "they fall in love with the product",
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... "but then it breaks",
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... "usually around the time the 90 day warranty expires",
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... "the twin towers collapsed today",
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... "However, Mr. Carter solemnly argues, his client carried out the kidnapping \
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... under orders and in the ''least offensive way possible.''"
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... ]
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>>> sentences.extend(tricky_sentences)
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>>> for sentence in sentences:
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... sid = SentimentIntensityAnalyzer()
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... print(sentence)
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... ss = sid.polarity_scores(sentence)
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... for k in sorted(ss):
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... print('{0}: {1}, '.format(k, ss[k]), end='')
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... print()
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VADER is smart, handsome, and funny.
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compound: 0.8316, neg: 0.0, neu: 0.254, pos: 0.746,
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VADER is smart, handsome, and funny!
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compound: 0.8439, neg: 0.0, neu: 0.248, pos: 0.752,
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VADER is very smart, handsome, and funny.
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compound: 0.8545, neg: 0.0, neu: 0.299, pos: 0.701,
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VADER is VERY SMART, handsome, and FUNNY.
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compound: 0.9227, neg: 0.0, neu: 0.246, pos: 0.754,
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VADER is VERY SMART, handsome, and FUNNY!!!
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compound: 0.9342, neg: 0.0, neu: 0.233, pos: 0.767,
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VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!
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compound: 0.9469, neg: 0.0, neu: 0.294, pos: 0.706,
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The book was good.
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compound: 0.4404, neg: 0.0, neu: 0.508, pos: 0.492,
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The book was kind of good.
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compound: 0.3832, neg: 0.0, neu: 0.657, pos: 0.343,
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The plot was good, but the characters are uncompelling and the dialog is not great.
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compound: -0.7042, neg: 0.327, neu: 0.579, pos: 0.094,
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A really bad, horrible book.
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compound: -0.8211, neg: 0.791, neu: 0.209, pos: 0.0,
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At least it isn't a horrible book.
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compound: 0.431, neg: 0.0, neu: 0.637, pos: 0.363,
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:) and :D
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compound: 0.7925, neg: 0.0, neu: 0.124, pos: 0.876,
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<BLANKLINE>
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compound: 0.0, neg: 0.0, neu: 0.0, pos: 0.0,
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Today sux
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compound: -0.3612, neg: 0.714, neu: 0.286, pos: 0.0,
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Today sux!
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compound: -0.4199, neg: 0.736, neu: 0.264, pos: 0.0,
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Today SUX!
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compound: -0.5461, neg: 0.779, neu: 0.221, pos: 0.0,
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Today kinda sux! But I'll get by, lol
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compound: 0.5249, neg: 0.138, neu: 0.517, pos: 0.344,
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It was one of the worst movies I've seen, despite good reviews.
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compound: -0.7584, neg: 0.394, neu: 0.606, pos: 0.0,
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Unbelievably bad acting!!
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compound: -0.6572, neg: 0.686, neu: 0.314, pos: 0.0,
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Poor direction.
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compound: -0.4767, neg: 0.756, neu: 0.244, pos: 0.0,
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VERY poor production.
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compound: -0.6281, neg: 0.674, neu: 0.326, pos: 0.0,
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The movie was bad.
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compound: -0.5423, neg: 0.538, neu: 0.462, pos: 0.0,
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Very bad movie.
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compound: -0.5849, neg: 0.655, neu: 0.345, pos: 0.0,
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VERY bad movie.
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compound: -0.6732, neg: 0.694, neu: 0.306, pos: 0.0,
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VERY BAD movie.
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compound: -0.7398, neg: 0.724, neu: 0.276, pos: 0.0,
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VERY BAD movie!
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compound: -0.7616, neg: 0.735, neu: 0.265, pos: 0.0,
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Most automated sentiment analysis tools are shit.
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compound: -0.5574, neg: 0.375, neu: 0.625, pos: 0.0,
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VADER sentiment analysis is the shit.
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compound: 0.6124, neg: 0.0, neu: 0.556, pos: 0.444,
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Sentiment analysis has never been good.
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compound: -0.3412, neg: 0.325, neu: 0.675, pos: 0.0,
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Sentiment analysis with VADER has never been this good.
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compound: 0.5228, neg: 0.0, neu: 0.703, pos: 0.297,
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Warren Beatty has never been so entertaining.
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compound: 0.5777, neg: 0.0, neu: 0.616, pos: 0.384,
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I won't say that the movie is astounding and I wouldn't claim that the movie is too banal either.
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compound: 0.4215, neg: 0.0, neu: 0.851, pos: 0.149,
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I like to hate Michael Bay films, but I couldn't fault this one
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compound: 0.3153, neg: 0.157, neu: 0.534, pos: 0.309,
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I like to hate Michael Bay films, BUT I couldn't help but fault this one
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compound: -0.1531, neg: 0.277, neu: 0.477, pos: 0.246,
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It's one thing to watch an Uwe Boll film, but another thing entirely to pay for it
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compound: -0.2541, neg: 0.112, neu: 0.888, pos: 0.0,
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The movie was too good
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compound: 0.4404, neg: 0.0, neu: 0.58, pos: 0.42,
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This movie was actually neither that funny, nor super witty.
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compound: -0.6759, neg: 0.41, neu: 0.59, pos: 0.0,
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This movie doesn't care about cleverness, wit or any other kind of intelligent humor.
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compound: -0.1338, neg: 0.265, neu: 0.497, pos: 0.239,
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Those who find ugly meanings in beautiful things are corrupt without being charming.
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compound: -0.3553, neg: 0.314, neu: 0.493, pos: 0.192,
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There are slow and repetitive parts, BUT it has just enough spice to keep it interesting.
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compound: 0.4678, neg: 0.079, neu: 0.735, pos: 0.186,
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The script is not fantastic, but the acting is decent and the cinematography is EXCELLENT!
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compound: 0.7565, neg: 0.092, neu: 0.607, pos: 0.301,
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Roger Dodger is one of the most compelling variations on this theme.
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compound: 0.2944, neg: 0.0, neu: 0.834, pos: 0.166,
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Roger Dodger is one of the least compelling variations on this theme.
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compound: -0.1695, neg: 0.132, neu: 0.868, pos: 0.0,
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Roger Dodger is at least compelling as a variation on the theme.
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compound: 0.2263, neg: 0.0, neu: 0.84, pos: 0.16,
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they fall in love with the product
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compound: 0.6369, neg: 0.0, neu: 0.588, pos: 0.412,
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but then it breaks
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compound: 0.0, neg: 0.0, neu: 1.0, pos: 0.0,
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usually around the time the 90 day warranty expires
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compound: 0.0, neg: 0.0, neu: 1.0, pos: 0.0,
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the twin towers collapsed today
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compound: -0.2732, neg: 0.344, neu: 0.656, pos: 0.0,
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However, Mr. Carter solemnly argues, his client carried out the kidnapping under orders and in the ''least offensive way possible.''
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compound: -0.5859, neg: 0.23, neu: 0.697, pos: 0.074,
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