Sentiment Analysis
>>> from nltk.classify import NaiveBayesClassifier >>> from nltk.corpus import subjectivity >>> from nltk.sentiment import SentimentAnalyzer >>> from nltk.sentiment.util import *
>>> n_instances = 100 >>> subj_docs = [(sent, ‘subj’) for sent in subjectivity.sents(categories=’subj’)[:n_instances]] >>> obj_docs = [(sent, ‘obj’) for sent in subjectivity.sents(categories=’obj’)[:n_instances]] >>> len(subj_docs), len(obj_docs) (100, 100)
Each document is represented by a tuple (sentence, label). The sentence is tokenized, so it is represented by a list of strings:
>>> subj_docs[0] ([‘smart’, ‘and’, ‘alert’, ‘,’, ‘thirteen’, ‘conversations’, ‘about’, ‘one’, ‘thing’, ‘is’, ‘a’, ‘small’, ‘gem’, ‘.’], ‘subj’)
We separately split subjective and objective instances to keep a balanced uniform class distribution in both train and test sets.
>>> train_subj_docs = subj_docs[:80] >>> test_subj_docs = subj_docs[80:100] >>> train_obj_docs = obj_docs[:80] >>> test_obj_docs = obj_docs[80:100] >>> training_docs = train_subj_docs+train_obj_docs >>> testing_docs = test_subj_docs+test_obj_docs
>>> sentim_analyzer = SentimentAnalyzer() >>> all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
We use simple unigram word features, handling negation:
>>> unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4) >>> len(unigram_feats) 83 >>> sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
We apply features to obtain a feature-value representation of our datasets:
>>> training_set = sentim_analyzer.apply_features(training_docs) >>> test_set = sentim_analyzer.apply_features(testing_docs)
We can now train our classifier on the training set, and subsequently output the evaluation results:
>>> trainer = NaiveBayesClassifier.train >>> classifier = sentim_analyzer.train(trainer, training_set) Training classifier >>> for key,value in sorted(sentim_analyzer.evaluate(test_set).items()): … print(‘{0}: {1}’.format(key, value)) Evaluating NaiveBayesClassifier results… Accuracy: 0.8 F-measure [obj]: 0.8 F-measure [subj]: 0.8 Precision [obj]: 0.8 Precision [subj]: 0.8 Recall [obj]: 0.8 Recall [subj]: 0.8
Vader
>>> from nltk.sentiment.vader import SentimentIntensityAnalyzer >>> sentences = [“VADER is smart, handsome, and funny.”, # positive sentence example … “VADER is smart, handsome, and funny!”, # punctuation emphasis handled correctly (sentiment intensity adjusted) … “VADER is very smart, handsome, and funny.”, # booster words handled correctly (sentiment intensity adjusted) … “VADER is VERY SMART, handsome, and FUNNY.”, # emphasis for ALLCAPS handled … “VADER is VERY SMART, handsome, and FUNNY!!!”,# combination of signals — VADER appropriately adjusts intensity … “VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!”,# booster words & punctuation make this close to ceiling for score … “The book was good.”, # positive sentence … “The book was kind of good.”, # qualified positive sentence is handled correctly (intensity adjusted) … “The plot was good, but the characters are uncompelling and the dialog is not great.”, # mixed negation sentence … “A really bad, horrible book.”, # negative sentence with booster words … “At least it isn’t a horrible book.”, # negated negative sentence with contraction … “:) and :D”, # emoticons handled … “”, # an empty string is correctly handled … “Today sux”, # negative slang handled … “Today sux!”, # negative slang with punctuation emphasis handled … “Today SUX!”, # negative slang with capitalization emphasis … “Today kinda sux! But I’ll get by, lol” # mixed sentiment example with slang and constrastive conjunction “but” … ] >>> paragraph = “It was one of the worst movies I’ve seen, despite good reviews. \ … Unbelievably bad acting!! Poor direction. VERY poor production. \ … The movie was bad. Very bad movie. VERY bad movie. VERY BAD movie. VERY BAD movie!”
>>> from nltk import tokenize >>> lines_list = tokenize.sent_tokenize(paragraph) >>> sentences.extend(lines_list)
>>> tricky_sentences = [ … “Most automated sentiment analysis tools are shit.”, … “VADER sentiment analysis is the shit.”, … “Sentiment analysis has never been good.”, … “Sentiment analysis with VADER has never been this good.”, … “Warren Beatty has never been so entertaining.”, … “I won’t say that the movie is astounding and I wouldn’t claim that \ … the movie is too banal either.”, … “I like to hate Michael Bay films, but I couldn’t fault this one”, … “It’s one thing to watch an Uwe Boll film, but another thing entirely \ … to pay for it”, … “The movie was too good”, … “This movie was actually neither that funny, nor super witty.”, … “This movie doesn’t care about cleverness, wit or any other kind of \ … intelligent humor.”, … “Those who find ugly meanings in beautiful things are corrupt without \ … being charming.”, … “There are slow and repetitive parts, BUT it has just enough spice to \ … keep it interesting.”, … “The script is not fantastic, but the acting is decent and the cinematography \ … is EXCELLENT!”, … “Roger Dodger is one of the most compelling variations on this theme.”, … “Roger Dodger is one of the least compelling variations on this theme.”, … “Roger Dodger is at least compelling as a variation on the theme.”, … “they fall in love with the product”, … “but then it breaks”, … “usually around the time the 90 day warranty expires”, … “the twin towers collapsed today”, … “However, Mr. Carter solemnly argues, his client carried out the kidnapping \ … under orders and in the ‘’least offensive way possible.’’” … ] >>> sentences.extend(tricky_sentences) >>> sid = SentimentIntensityAnalyzer() >>> for sentence in sentences: … print(sentence) … ss = sid.polarity_scores(sentence) … for k in sorted(ss): … print(‘{0}: {1}, ‘.format(k, ss[k]), end=’’) … print() VADER is smart, handsome, and funny. compound: 0.8316, neg: 0.0, neu: 0.254, pos: 0.746, VADER is smart, handsome, and funny! compound: 0.8439, neg: 0.0, neu: 0.248, pos: 0.752, VADER is very smart, handsome, and funny. compound: 0.8545, neg: 0.0, neu: 0.299, pos: 0.701, VADER is VERY SMART, handsome, and FUNNY. compound: 0.9227, neg: 0.0, neu: 0.246, pos: 0.754, VADER is VERY SMART, handsome, and FUNNY!!! compound: 0.9342, neg: 0.0, neu: 0.233, pos: 0.767, VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!! compound: 0.9469, neg: 0.0, neu: 0.294, pos: 0.706, The book was good. compound: 0.4404, neg: 0.0, neu: 0.508, pos: 0.492, The book was kind of good. compound: 0.3832, neg: 0.0, neu: 0.657, pos: 0.343, The plot was good, but the characters are uncompelling and the dialog is not great. compound: -0.7042, neg: 0.327, neu: 0.579, pos: 0.094, A really bad, horrible book. compound: -0.8211, neg: 0.791, neu: 0.209, pos: 0.0, At least it isn’t a horrible book. compound: 0.431, neg: 0.0, neu: 0.637, pos: 0.363, :) and :D compound: 0.7925, neg: 0.0, neu: 0.124, pos: 0.876, <BLANKLINE> compound: 0.0, neg: 0.0, neu: 0.0, pos: 0.0, Today sux compound: -0.3612, neg: 0.714, neu: 0.286, pos: 0.0, Today sux! compound: -0.4199, neg: 0.736, neu: 0.264, pos: 0.0, Today SUX! compound: -0.5461, neg: 0.779, neu: 0.221, pos: 0.0, Today kinda sux! But I’ll get by, lol compound: 0.2228, neg: 0.195, neu: 0.531, pos: 0.274, It was one of the worst movies I’ve seen, despite good reviews. compound: -0.7584, neg: 0.394, neu: 0.606, pos: 0.0, Unbelievably bad acting!! compound: -0.6572, neg: 0.686, neu: 0.314, pos: 0.0, Poor direction. compound: -0.4767, neg: 0.756, neu: 0.244, pos: 0.0, VERY poor production. compound: -0.6281, neg: 0.674, neu: 0.326, pos: 0.0, The movie was bad. compound: -0.5423, neg: 0.538, neu: 0.462, pos: 0.0, Very bad movie. compound: -0.5849, neg: 0.655, neu: 0.345, pos: 0.0, VERY bad movie. compound: -0.6732, neg: 0.694, neu: 0.306, pos: 0.0, VERY BAD movie. compound: -0.7398, neg: 0.724, neu: 0.276, pos: 0.0, VERY BAD movie! compound: -0.7616, neg: 0.735, neu: 0.265, pos: 0.0, Most automated sentiment analysis tools are shit. compound: -0.5574, neg: 0.375, neu: 0.625, pos: 0.0, VADER sentiment analysis is the shit. compound: 0.6124, neg: 0.0, neu: 0.556, pos: 0.444, Sentiment analysis has never been good. compound: -0.3412, neg: 0.325, neu: 0.675, pos: 0.0, Sentiment analysis with VADER has never been this good. compound: 0.5228, neg: 0.0, neu: 0.703, pos: 0.297, Warren Beatty has never been so entertaining. compound: 0.5777, neg: 0.0, neu: 0.616, pos: 0.384, I won’t say that the movie is astounding and I wouldn’t claim that the movie is too banal either. compound: 0.4215, neg: 0.0, neu: 0.851, pos: 0.149, I like to hate Michael Bay films, but I couldn’t fault this one compound: 0.3153, neg: 0.157, neu: 0.534, pos: 0.309, It’s one thing to watch an Uwe Boll film, but another thing entirely to pay for it compound: -0.2541, neg: 0.112, neu: 0.888, pos: 0.0, The movie was too good compound: 0.4404, neg: 0.0, neu: 0.58, pos: 0.42, This movie was actually neither that funny, nor super witty. compound: -0.6759, neg: 0.41, neu: 0.59, pos: 0.0, This movie doesn’t care about cleverness, wit or any other kind of intelligent humor. compound: -0.1338, neg: 0.265, neu: 0.497, pos: 0.239, Those who find ugly meanings in beautiful things are corrupt without being charming. compound: -0.3553, neg: 0.314, neu: 0.493, pos: 0.192, There are slow and repetitive parts, BUT it has just enough spice to keep it interesting. compound: 0.4678, neg: 0.079, neu: 0.735, pos: 0.186, The script is not fantastic, but the acting is decent and the cinematography is EXCELLENT! compound: 0.7565, neg: 0.092, neu: 0.607, pos: 0.301, Roger Dodger is one of the most compelling variations on this theme. compound: 0.2944, neg: 0.0, neu: 0.834, pos: 0.166, Roger Dodger is one of the least compelling variations on this theme. compound: -0.1695, neg: 0.132, neu: 0.868, pos: 0.0, Roger Dodger is at least compelling as a variation on the theme. compound: 0.2263, neg: 0.0, neu: 0.84, pos: 0.16, they fall in love with the product compound: 0.6369, neg: 0.0, neu: 0.588, pos: 0.412, but then it breaks compound: 0.0, neg: 0.0, neu: 1.0, pos: 0.0, usually around the time the 90 day warranty expires compound: 0.0, neg: 0.0, neu: 1.0, pos: 0.0, the twin towers collapsed today compound: -0.2732, neg: 0.344, neu: 0.656, pos: 0.0, However, Mr. Carter solemnly argues, his client carried out the kidnapping under orders and in the ‘’least offensive way possible.’’ compound: -0.5859, neg: 0.23, neu: 0.697, pos: 0.074,
