# # Natural Language Toolkit: Sentiment Analyzer # # Copyright (C) 2001-2023 NLTK Project # Author: Pierpaolo Pantone <24alsecondo@gmail.com> # URL: # For license information, see LICENSE.TXT """ Utility methods for Sentiment Analysis. """ import codecs import csv import json import pickle import random import re import sys import time from copy import deepcopy import nltk from nltk.corpus import CategorizedPlaintextCorpusReader from nltk.data import load from nltk.tokenize.casual import EMOTICON_RE # //////////////////////////////////////////////////////////// # { Regular expressions # //////////////////////////////////////////////////////////// # Regular expression for negation by Christopher Potts NEGATION = r""" (?: ^(?:never|no|nothing|nowhere|noone|none|not| havent|hasnt|hadnt|cant|couldnt|shouldnt| wont|wouldnt|dont|doesnt|didnt|isnt|arent|aint )$ ) | n't""" NEGATION_RE = re.compile(NEGATION, re.VERBOSE) CLAUSE_PUNCT = r"^[.:;!?]$" CLAUSE_PUNCT_RE = re.compile(CLAUSE_PUNCT) # Happy and sad emoticons HAPPY = { ":-)", ":)", ";)", ":o)", ":]", ":3", ":c)", ":>", "=]", "8)", "=)", ":}", ":^)", ":-D", ":D", "8-D", "8D", "x-D", "xD", "X-D", "XD", "=-D", "=D", "=-3", "=3", ":-))", ":'-)", ":')", ":*", ":^*", ">:P", ":-P", ":P", "X-P", "x-p", "xp", "XP", ":-p", ":p", "=p", ":-b", ":b", ">:)", ">;)", ">:-)", "<3", } SAD = { ":L", ":-/", ">:/", ":S", ">:[", ":@", ":-(", ":[", ":-||", "=L", ":<", ":-[", ":-<", "=\\", "=/", ">:(", ":(", ">.<", ":'-(", ":'(", ":\\", ":-c", ":c", ":{", ">:\\", ";(", } def timer(method): """ A timer decorator to measure execution performance of methods. """ def timed(*args, **kw): start = time.time() result = method(*args, **kw) end = time.time() tot_time = end - start hours = tot_time // 3600 mins = tot_time // 60 % 60 # in Python 2.x round() will return a float, so we convert it to int secs = int(round(tot_time % 60)) if hours == 0 and mins == 0 and secs < 10: print(f"[TIMER] {method.__name__}(): {method.__name__:.3f} seconds") else: print(f"[TIMER] {method.__name__}(): {hours}h {mins}m {secs}s") return result return timed # //////////////////////////////////////////////////////////// # { Feature extractor functions # //////////////////////////////////////////////////////////// """ Feature extractor functions are declared outside the SentimentAnalyzer class. Users should have the possibility to create their own feature extractors without modifying SentimentAnalyzer. """ def extract_unigram_feats(document, unigrams, handle_negation=False): """ Populate a dictionary of unigram features, reflecting the presence/absence in the document of each of the tokens in `unigrams`. :param document: a list of words/tokens. :param unigrams: a list of words/tokens whose presence/absence has to be checked in `document`. :param handle_negation: if `handle_negation == True` apply `mark_negation` method to `document` before checking for unigram presence/absence. :return: a dictionary of unigram features {unigram : boolean}. >>> words = ['ice', 'police', 'riot'] >>> document = 'ice is melting due to global warming'.split() >>> sorted(extract_unigram_feats(document, words).items()) [('contains(ice)', True), ('contains(police)', False), ('contains(riot)', False)] """ features = {} if handle_negation: document = mark_negation(document) for word in unigrams: features[f"contains({word})"] = word in set(document) return features def extract_bigram_feats(document, bigrams): """ Populate a dictionary of bigram features, reflecting the presence/absence in the document of each of the tokens in `bigrams`. This extractor function only considers contiguous bigrams obtained by `nltk.bigrams`. :param document: a list of words/tokens. :param unigrams: a list of bigrams whose presence/absence has to be checked in `document`. :return: a dictionary of bigram features {bigram : boolean}. >>> bigrams = [('global', 'warming'), ('police', 'prevented'), ('love', 'you')] >>> document = 'ice is melting due to global warming'.split() >>> sorted(extract_bigram_feats(document, bigrams).items()) # doctest: +NORMALIZE_WHITESPACE [('contains(global - warming)', True), ('contains(love - you)', False), ('contains(police - prevented)', False)] """ features = {} for bigr in bigrams: features[f"contains({bigr[0]} - {bigr[1]})"] = bigr in nltk.bigrams(document) return features # //////////////////////////////////////////////////////////// # { Helper Functions # //////////////////////////////////////////////////////////// def mark_negation(document, double_neg_flip=False, shallow=False): """ Append _NEG suffix to words that appear in the scope between a negation and a punctuation mark. :param document: a list of words/tokens, or a tuple (words, label). :param shallow: if True, the method will modify the original document in place. :param double_neg_flip: if True, double negation is considered affirmation (we activate/deactivate negation scope every time we find a negation). :return: if `shallow == True` the method will modify the original document and return it. If `shallow == False` the method will return a modified document, leaving the original unmodified. >>> sent = "I didn't like this movie . It was bad .".split() >>> mark_negation(sent) ['I', "didn't", 'like_NEG', 'this_NEG', 'movie_NEG', '.', 'It', 'was', 'bad', '.'] """ if not shallow: document = deepcopy(document) # check if the document is labeled. If so, do not consider the label. labeled = document and isinstance(document[0], (tuple, list)) if labeled: doc = document[0] else: doc = document neg_scope = False for i, word in enumerate(doc): if NEGATION_RE.search(word): if not neg_scope or (neg_scope and double_neg_flip): neg_scope = not neg_scope continue else: doc[i] += "_NEG" elif neg_scope and CLAUSE_PUNCT_RE.search(word): neg_scope = not neg_scope elif neg_scope and not CLAUSE_PUNCT_RE.search(word): doc[i] += "_NEG" return document def output_markdown(filename, **kwargs): """ Write the output of an analysis to a file. """ with codecs.open(filename, "at") as outfile: text = "\n*** \n\n" text += "{} \n\n".format(time.strftime("%d/%m/%Y, %H:%M")) for k in sorted(kwargs): if isinstance(kwargs[k], dict): dictionary = kwargs[k] text += f" - **{k}:**\n" for entry in sorted(dictionary): text += f" - {entry}: {dictionary[entry]} \n" elif isinstance(kwargs[k], list): text += f" - **{k}:**\n" for entry in kwargs[k]: text += f" - {entry}\n" else: text += f" - **{k}:** {kwargs[k]} \n" outfile.write(text) def split_train_test(all_instances, n=None): """ Randomly split `n` instances of the dataset into train and test sets. :param all_instances: a list of instances (e.g. documents) that will be split. :param n: the number of instances to consider (in case we want to use only a subset). :return: two lists of instances. Train set is 8/10 of the total and test set is 2/10 of the total. """ random.seed(12345) random.shuffle(all_instances) if not n or n > len(all_instances): n = len(all_instances) train_set = all_instances[: int(0.8 * n)] test_set = all_instances[int(0.8 * n) : n] return train_set, test_set def _show_plot(x_values, y_values, x_labels=None, y_labels=None): try: import matplotlib.pyplot as plt except ImportError as e: raise ImportError( "The plot function requires matplotlib to be installed." "See https://matplotlib.org/" ) from e plt.locator_params(axis="y", nbins=3) axes = plt.axes() axes.yaxis.grid() plt.plot(x_values, y_values, "ro", color="red") plt.ylim(ymin=-1.2, ymax=1.2) plt.tight_layout(pad=5) if x_labels: plt.xticks(x_values, x_labels, rotation="vertical") if y_labels: plt.yticks([-1, 0, 1], y_labels, rotation="horizontal") # Pad margins so that markers are not clipped by the axes plt.margins(0.2) plt.show() # //////////////////////////////////////////////////////////// # { Parsing and conversion functions # //////////////////////////////////////////////////////////// def json2csv_preprocess( json_file, outfile, fields, encoding="utf8", errors="replace", gzip_compress=False, skip_retweets=True, skip_tongue_tweets=True, skip_ambiguous_tweets=True, strip_off_emoticons=True, remove_duplicates=True, limit=None, ): """ Convert json file to csv file, preprocessing each row to obtain a suitable dataset for tweets Semantic Analysis. :param json_file: the original json file containing tweets. :param outfile: the output csv filename. :param fields: a list of fields that will be extracted from the json file and kept in the output csv file. :param encoding: the encoding of the files. :param errors: the error handling strategy for the output writer. :param gzip_compress: if True, create a compressed GZIP file. :param skip_retweets: if True, remove retweets. :param skip_tongue_tweets: if True, remove tweets containing ":P" and ":-P" emoticons. :param skip_ambiguous_tweets: if True, remove tweets containing both happy and sad emoticons. :param strip_off_emoticons: if True, strip off emoticons from all tweets. :param remove_duplicates: if True, remove tweets appearing more than once. :param limit: an integer to set the number of tweets to convert. After the limit is reached the conversion will stop. It can be useful to create subsets of the original tweets json data. """ with codecs.open(json_file, encoding=encoding) as fp: (writer, outf) = _outf_writer(outfile, encoding, errors, gzip_compress) # write the list of fields as header writer.writerow(fields) if remove_duplicates == True: tweets_cache = [] i = 0 for line in fp: tweet = json.loads(line) row = extract_fields(tweet, fields) try: text = row[fields.index("text")] # Remove retweets if skip_retweets == True: if re.search(r"\bRT\b", text): continue # Remove tweets containing ":P" and ":-P" emoticons if skip_tongue_tweets == True: if re.search(r"\:\-?P\b", text): continue # Remove tweets containing both happy and sad emoticons if skip_ambiguous_tweets == True: all_emoticons = EMOTICON_RE.findall(text) if all_emoticons: if (set(all_emoticons) & HAPPY) and (set(all_emoticons) & SAD): continue # Strip off emoticons from all tweets if strip_off_emoticons == True: row[fields.index("text")] = re.sub( r"(?!\n)\s+", " ", EMOTICON_RE.sub("", text) ) # Remove duplicate tweets if remove_duplicates == True: if row[fields.index("text")] in tweets_cache: continue else: tweets_cache.append(row[fields.index("text")]) except ValueError: pass writer.writerow(row) i += 1 if limit and i >= limit: break outf.close() def parse_tweets_set( filename, label, word_tokenizer=None, sent_tokenizer=None, skip_header=True ): """ Parse csv file containing tweets and output data a list of (text, label) tuples. :param filename: the input csv filename. :param label: the label to be appended to each tweet contained in the csv file. :param word_tokenizer: the tokenizer instance that will be used to tokenize each sentence into tokens (e.g. WordPunctTokenizer() or BlanklineTokenizer()). If no word_tokenizer is specified, tweets will not be tokenized. :param sent_tokenizer: the tokenizer that will be used to split each tweet into sentences. :param skip_header: if True, skip the first line of the csv file (which usually contains headers). :return: a list of (text, label) tuples. """ tweets = [] if not sent_tokenizer: sent_tokenizer = load("tokenizers/punkt/english.pickle") with codecs.open(filename, "rt") as csvfile: reader = csv.reader(csvfile) if skip_header == True: next(reader, None) # skip the header i = 0 for tweet_id, text in reader: # text = text[1] i += 1 sys.stdout.write(f"Loaded {i} tweets\r") # Apply sentence and word tokenizer to text if word_tokenizer: tweet = [ w for sent in sent_tokenizer.tokenize(text) for w in word_tokenizer.tokenize(sent) ] else: tweet = text tweets.append((tweet, label)) print(f"Loaded {i} tweets") return tweets # //////////////////////////////////////////////////////////// # { Demos # //////////////////////////////////////////////////////////// def demo_tweets(trainer, n_instances=None, output=None): """ Train and test Naive Bayes classifier on 10000 tweets, tokenized using TweetTokenizer. Features are composed of: - 1000 most frequent unigrams - 100 top bigrams (using BigramAssocMeasures.pmi) :param trainer: `train` method of a classifier. :param n_instances: the number of total tweets that have to be used for training and testing. Tweets will be equally split between positive and negative. :param output: the output file where results have to be reported. """ from nltk.corpus import stopwords, twitter_samples from nltk.sentiment import SentimentAnalyzer from nltk.tokenize import TweetTokenizer # Different customizations for the TweetTokenizer tokenizer = TweetTokenizer(preserve_case=False) # tokenizer = TweetTokenizer(preserve_case=True, strip_handles=True) # tokenizer = TweetTokenizer(reduce_len=True, strip_handles=True) if n_instances is not None: n_instances = int(n_instances / 2) fields = ["id", "text"] positive_json = twitter_samples.abspath("positive_tweets.json") positive_csv = "positive_tweets.csv" json2csv_preprocess(positive_json, positive_csv, fields, limit=n_instances) negative_json = twitter_samples.abspath("negative_tweets.json") negative_csv = "negative_tweets.csv" json2csv_preprocess(negative_json, negative_csv, fields, limit=n_instances) neg_docs = parse_tweets_set(negative_csv, label="neg", word_tokenizer=tokenizer) pos_docs = parse_tweets_set(positive_csv, label="pos", word_tokenizer=tokenizer) # We separately split subjective and objective instances to keep a balanced # uniform class distribution in both train and test sets. train_pos_docs, test_pos_docs = split_train_test(pos_docs) train_neg_docs, test_neg_docs = split_train_test(neg_docs) training_tweets = train_pos_docs + train_neg_docs testing_tweets = test_pos_docs + test_neg_docs sentim_analyzer = SentimentAnalyzer() # stopwords = stopwords.words('english') # all_words = [word for word in sentim_analyzer.all_words(training_tweets) if word.lower() not in stopwords] all_words = [word for word in sentim_analyzer.all_words(training_tweets)] # Add simple unigram word features unigram_feats = sentim_analyzer.unigram_word_feats(all_words, top_n=1000) sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats) # Add bigram collocation features bigram_collocs_feats = sentim_analyzer.bigram_collocation_feats( [tweet[0] for tweet in training_tweets], top_n=100, min_freq=12 ) sentim_analyzer.add_feat_extractor( extract_bigram_feats, bigrams=bigram_collocs_feats ) training_set = sentim_analyzer.apply_features(training_tweets) test_set = sentim_analyzer.apply_features(testing_tweets) classifier = sentim_analyzer.train(trainer, training_set) # classifier = sentim_analyzer.train(trainer, training_set, max_iter=4) try: classifier.show_most_informative_features() except AttributeError: print( "Your classifier does not provide a show_most_informative_features() method." ) results = sentim_analyzer.evaluate(test_set) if output: extr = [f.__name__ for f in sentim_analyzer.feat_extractors] output_markdown( output, Dataset="labeled_tweets", Classifier=type(classifier).__name__, Tokenizer=tokenizer.__class__.__name__, Feats=extr, Results=results, Instances=n_instances, ) def demo_movie_reviews(trainer, n_instances=None, output=None): """ Train classifier on all instances of the Movie Reviews dataset. The corpus has been preprocessed using the default sentence tokenizer and WordPunctTokenizer. Features are composed of: - most frequent unigrams :param trainer: `train` method of a classifier. :param n_instances: the number of total reviews that have to be used for training and testing. Reviews will be equally split between positive and negative. :param output: the output file where results have to be reported. """ from nltk.corpus import movie_reviews from nltk.sentiment import SentimentAnalyzer if n_instances is not None: n_instances = int(n_instances / 2) pos_docs = [ (list(movie_reviews.words(pos_id)), "pos") for pos_id in movie_reviews.fileids("pos")[:n_instances] ] neg_docs = [ (list(movie_reviews.words(neg_id)), "neg") for neg_id in movie_reviews.fileids("neg")[:n_instances] ] # We separately split positive and negative instances to keep a balanced # uniform class distribution in both train and test sets. train_pos_docs, test_pos_docs = split_train_test(pos_docs) train_neg_docs, test_neg_docs = split_train_test(neg_docs) training_docs = train_pos_docs + train_neg_docs testing_docs = test_pos_docs + test_neg_docs sentim_analyzer = SentimentAnalyzer() all_words = sentim_analyzer.all_words(training_docs) # Add simple unigram word features unigram_feats = sentim_analyzer.unigram_word_feats(all_words, min_freq=4) sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats) # 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) classifier = sentim_analyzer.train(trainer, training_set) try: classifier.show_most_informative_features() except AttributeError: print( "Your classifier does not provide a show_most_informative_features() method." ) results = sentim_analyzer.evaluate(test_set) if output: extr = [f.__name__ for f in sentim_analyzer.feat_extractors] output_markdown( output, Dataset="Movie_reviews", Classifier=type(classifier).__name__, Tokenizer="WordPunctTokenizer", Feats=extr, Results=results, Instances=n_instances, ) def demo_subjectivity(trainer, save_analyzer=False, n_instances=None, output=None): """ Train and test a classifier on instances of the Subjective Dataset by Pang and Lee. The dataset is made of 5000 subjective and 5000 objective sentences. All tokens (words and punctuation marks) are separated by a whitespace, so we use the basic WhitespaceTokenizer to parse the data. :param trainer: `train` method of a classifier. :param save_analyzer: if `True`, store the SentimentAnalyzer in a pickle file. :param n_instances: the number of total sentences that have to be used for training and testing. Sentences will be equally split between positive and negative. :param output: the output file where results have to be reported. """ from nltk.corpus import subjectivity from nltk.sentiment import SentimentAnalyzer if n_instances is not None: n_instances = int(n_instances / 2) 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] ] # We separately split subjective and objective instances to keep a balanced # uniform class distribution in both train and test sets. train_subj_docs, test_subj_docs = split_train_test(subj_docs) train_obj_docs, test_obj_docs = split_train_test(obj_docs) 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] ) # Add simple unigram word features handling negation unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4) sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats) # 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) classifier = sentim_analyzer.train(trainer, training_set) try: classifier.show_most_informative_features() except AttributeError: print( "Your classifier does not provide a show_most_informative_features() method." ) results = sentim_analyzer.evaluate(test_set) if save_analyzer == True: sentim_analyzer.save_file(sentim_analyzer, "sa_subjectivity.pickle") if output: extr = [f.__name__ for f in sentim_analyzer.feat_extractors] output_markdown( output, Dataset="subjectivity", Classifier=type(classifier).__name__, Tokenizer="WhitespaceTokenizer", Feats=extr, Instances=n_instances, Results=results, ) return sentim_analyzer def demo_sent_subjectivity(text): """ Classify a single sentence as subjective or objective using a stored SentimentAnalyzer. :param text: a sentence whose subjectivity has to be classified. """ from nltk.classify import NaiveBayesClassifier from nltk.tokenize import regexp word_tokenizer = regexp.WhitespaceTokenizer() try: sentim_analyzer = load("sa_subjectivity.pickle") except LookupError: print("Cannot find the sentiment analyzer you want to load.") print("Training a new one using NaiveBayesClassifier.") sentim_analyzer = demo_subjectivity(NaiveBayesClassifier.train, True) # Tokenize and convert to lower case tokenized_text = [word.lower() for word in word_tokenizer.tokenize(text)] print(sentim_analyzer.classify(tokenized_text)) def demo_liu_hu_lexicon(sentence, plot=False): """ Basic example of sentiment classification using Liu and Hu opinion lexicon. This function simply counts the number of positive, negative and neutral words in the sentence and classifies it depending on which polarity is more represented. Words that do not appear in the lexicon are considered as neutral. :param sentence: a sentence whose polarity has to be classified. :param plot: if True, plot a visual representation of the sentence polarity. """ from nltk.corpus import opinion_lexicon from nltk.tokenize import treebank tokenizer = treebank.TreebankWordTokenizer() pos_words = 0 neg_words = 0 tokenized_sent = [word.lower() for word in tokenizer.tokenize(sentence)] x = list(range(len(tokenized_sent))) # x axis for the plot y = [] for word in tokenized_sent: if word in opinion_lexicon.positive(): pos_words += 1 y.append(1) # positive elif word in opinion_lexicon.negative(): neg_words += 1 y.append(-1) # negative else: y.append(0) # neutral if pos_words > neg_words: print("Positive") elif pos_words < neg_words: print("Negative") elif pos_words == neg_words: print("Neutral") if plot == True: _show_plot( x, y, x_labels=tokenized_sent, y_labels=["Negative", "Neutral", "Positive"] ) def demo_vader_instance(text): """ Output polarity scores for a text using Vader approach. :param text: a text whose polarity has to be evaluated. """ from nltk.sentiment import SentimentIntensityAnalyzer vader_analyzer = SentimentIntensityAnalyzer() print(vader_analyzer.polarity_scores(text)) def demo_vader_tweets(n_instances=None, output=None): """ Classify 10000 positive and negative tweets using Vader approach. :param n_instances: the number of total tweets that have to be classified. :param output: the output file where results have to be reported. """ from collections import defaultdict from nltk.corpus import twitter_samples from nltk.metrics import accuracy as eval_accuracy from nltk.metrics import f_measure as eval_f_measure from nltk.metrics import precision as eval_precision from nltk.metrics import recall as eval_recall from nltk.sentiment import SentimentIntensityAnalyzer if n_instances is not None: n_instances = int(n_instances / 2) fields = ["id", "text"] positive_json = twitter_samples.abspath("positive_tweets.json") positive_csv = "positive_tweets.csv" json2csv_preprocess( positive_json, positive_csv, fields, strip_off_emoticons=False, limit=n_instances, ) negative_json = twitter_samples.abspath("negative_tweets.json") negative_csv = "negative_tweets.csv" json2csv_preprocess( negative_json, negative_csv, fields, strip_off_emoticons=False, limit=n_instances, ) pos_docs = parse_tweets_set(positive_csv, label="pos") neg_docs = parse_tweets_set(negative_csv, label="neg") # We separately split subjective and objective instances to keep a balanced # uniform class distribution in both train and test sets. train_pos_docs, test_pos_docs = split_train_test(pos_docs) train_neg_docs, test_neg_docs = split_train_test(neg_docs) training_tweets = train_pos_docs + train_neg_docs testing_tweets = test_pos_docs + test_neg_docs vader_analyzer = SentimentIntensityAnalyzer() gold_results = defaultdict(set) test_results = defaultdict(set) acc_gold_results = [] acc_test_results = [] labels = set() num = 0 for i, (text, label) in enumerate(testing_tweets): labels.add(label) gold_results[label].add(i) acc_gold_results.append(label) score = vader_analyzer.polarity_scores(text)["compound"] if score > 0: observed = "pos" else: observed = "neg" num += 1 acc_test_results.append(observed) test_results[observed].add(i) metrics_results = {} for label in labels: accuracy_score = eval_accuracy(acc_gold_results, acc_test_results) metrics_results["Accuracy"] = accuracy_score precision_score = eval_precision(gold_results[label], test_results[label]) metrics_results[f"Precision [{label}]"] = precision_score recall_score = eval_recall(gold_results[label], test_results[label]) metrics_results[f"Recall [{label}]"] = recall_score f_measure_score = eval_f_measure(gold_results[label], test_results[label]) metrics_results[f"F-measure [{label}]"] = f_measure_score for result in sorted(metrics_results): print(f"{result}: {metrics_results[result]}") if output: output_markdown( output, Approach="Vader", Dataset="labeled_tweets", Instances=n_instances, Results=metrics_results, ) if __name__ == "__main__": from sklearn.svm import LinearSVC from nltk.classify import MaxentClassifier, NaiveBayesClassifier from nltk.classify.scikitlearn import SklearnClassifier from nltk.twitter.common import _outf_writer, extract_fields naive_bayes = NaiveBayesClassifier.train svm = SklearnClassifier(LinearSVC()).train maxent = MaxentClassifier.train demo_tweets(naive_bayes) # demo_movie_reviews(svm) # demo_subjectivity(svm) # demo_sent_subjectivity("she's an artist , but hasn't picked up a brush in a year . ") # demo_liu_hu_lexicon("This movie was actually neither that funny, nor super witty.", plot=True) # demo_vader_instance("This movie was actually neither that funny, nor super witty.") # demo_vader_tweets()