Python regexp.WhitespaceTokenizer方法代碼示例

# 需要導入模塊: from nltk.tokenize import regexp [as 別名]
# 或者: from nltk.tokenize.regexp import WhitespaceTokenizer [as 別名]
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)) 

# 需要導入模塊: from nltk.tokenize import regexp [as 別名]
# 或者: from nltk.tokenize.regexp import WhitespaceTokenizer [as 別名]
def __init__(self, use_unicode):
        self.repeat_regexp = re.compile(r'(\w*)(\w)\2(\w*)')
        self.repl = r'\1\2\3'
        self.tokenizer = WhitespaceTokenizer()
        self.cached_stopwords = stopwords.words('english')
        self.symbols = [u"\"", u"'", u"!", u"?", u".", u",", u";", u">", u"_", u"<", u"-", u"[",
                        u"]", u"{", u"}", u"/", u"\\", u"^", u"~", u"", u"`", u"``", u"\u2026",
                        u":", u"(", u")", u"|", u"#", u"$", u"%", u"&", u"*", u"=", u"+", u"\u2013",
                        u"\u201c", u"\u201d", u"\u300b\u300b", u"\u2019", u"\u2018", u"\u00b0",
                        u"\u00ba", u"\u200b", u"\u00b7", u"\u2014", u"\u00bb", u"\u221a", u"\u00aa",
                        u"\ufe0f", u"\u2794", u"\u2192", u"\u00a8", u"\u2022", u"\u300a", u"\u00bf",
                        u"\u25a0", u"\u00af", u"\u22b3", u"\u2060", u"\u261b", u"\u00ad", u"\u00ab"]
        if use_unicode:
            self.accents = unicode_replace
        else:
            self.accents = ascii_replace
        self.link_patterns = [('http'), ('www'), ('w3c')]
        self.digraph = [(r'hash','#'),(r'rxr','rr'),(r'sxs','ss'),(r'aqa','aa'),(r'eqe','ee'),(r'oqo','oo'),(r'fqf','ff'),(r'gqg','gg'),(r'cqc','cc'),(r'dqd','dd'),
                        (r'mqm','mm'),(r'nqn','nn'),(r'pqp','pp'),(r'dqd','dd'),(r'tqt','tt'),(r'fqf','ff'),(r'lql','ll')]

    # Remover caracteres repetidos seguidamente, para que o modelo no seja prejudicado
    # por falta de padro na escrita. 

# 需要導入模塊: from nltk.tokenize import regexp [as 別名]
# 或者: from nltk.tokenize.regexp import WhitespaceTokenizer [as 別名]
def __init__(self, use_unicode=True):
        self.repeat_regexp = re.compile(r'(\w*)(\w)\2(\w*)')
        self.repl = r'\1\2\3'
        self.pt_stemmer = nltk.stem.RSLPStemmer()
        self.tokenizer = WhitespaceTokenizer()
        self.cached_stopwords = stopwords.words('portuguese')
        self.symbols = [u"\"", u"'", u"!", u"?", u".", u",", u";", u">", u"_", u"<", u"-", u"[",
                        u"]", u"{", u"}", u"/", u"\\", u"^", u"~", u"",  u"`", u"``", u"\u2026",
                        u":", u"(", u")", u"|", u"#", u"$", u"%", u"&", u"*", u"=", u"+", u"\u2013",
                        u"\u201c", u"\u201d", u"\u300b", u"\u2019", u"\u2018", u"\u00b0", u"\u30fb",
                        u"\u00ba", u"\u200b", u"\u00b7", u"\u2014", u"\u00bb", u"\u221a", u"\u00aa",
                        u"\ufe0f", u"\u2794", u"\u2192", u"\u00a8", u"\u2022", u"\u300a", u"\u00bf",
                        u"\u25a0", u"\u00af", u"\u22b3", u"\u2060", u"\u261b", u"\u00ad", u"\u00ab"]
        self.more_stopwords = ['ja', 'q', 'd', 'ai', 'desse', 'dessa', 'disso', 'nesse', 'nessa', 'nisso', 'esse', 'essa', 'isso', 'so', 'mt', 'vc', 'voce', 'ne', 'ta', 'to', 'pq',
                               'cade', 'kd', 'la', 'e', 'eh', 'dai', 'pra', 'vai', 'olha', 'pois', 'rt', 'retweeted',
                               'fica', 'muito', 'muita', 'muitos', 'muitas', 'onde', 'mim', 'oi', 'ola', 'ate']
        if use_unicode:
            self.accents = unicode_replace
        else:
            self.accents = ascii_replace
        self.link_patterns = [('http'), ('www'), ('w3c'), ('https')]
        self.normal = [(r'kxkxk', 'kkk'), (r'nao ', ' nao_'), (r' ir ', '_ir '), (r'bom demal', ' bomdemais '), (r'\s*insan\s*', ' insano '), (r'\s*saudad\s*', ' saudade ')]
        self.digraph = [(r'rxr', 'rr'), (r'sxs', 'ss'), (r'aqa', 'aa'), (r'eqe', 'ee'), (r'oqo', 'oo')]

    # Remover caracteres repetidos seguidamente, para que o modelo no seja prejudicado
    # por falta de padro na escrita. 

# 需要導入模塊: from nltk.tokenize import regexp [as 別名]
# 或者: from nltk.tokenize.regexp import WhitespaceTokenizer [as 別名]
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 

# 需要導入模塊: from nltk.tokenize import regexp [as 別名]
# 或者: from nltk.tokenize.regexp import WhitespaceTokenizer [as 別名]
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 sentiment_analyzer 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