python类sentiment()的实例源码

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_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_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_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_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_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_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_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_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_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_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_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.sentiment import SentimentAnalyzer
    from nltk.corpus import subjectivity

    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:
        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_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.sentiment import SentimentAnalyzer
    from nltk.corpus import subjectivity

    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:
        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_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.sentiment import SentimentAnalyzer
    from nltk.corpus import subjectivity

    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:
        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_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.sentiment import SentimentAnalyzer
    from nltk.corpus import subjectivity

    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:
        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_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.sentiment import SentimentAnalyzer
    from nltk.corpus import subjectivity

    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:
        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_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.sentiment import SentimentAnalyzer
    from nltk.corpus import subjectivity

    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:
        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