Python nltk 模块，tokenize() 实例源码

def tokenize_text( sample_text ):
    global sequence_lengths
    processed_text = []

    if cfg.remove_punctuation:
        cleaned = sample_text.lower().translate( t_table )
    else:
        cleaned = sample_text

    if cfg.use_casual_tokenizer:
        tokens = tknzr.tokenize( cleaned )
    else:
        tokens = nltk.word_tokenize( cleaned, language='english')

    if cfg.remove_stopwords:
        tokens = [w for w in tokens if not w in stopwords.words('english')]

    sequence_lengths.append( len( tokens ) )
    processed_text.extend( tokens )

    return processed_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 get_sentences(text=''):
    tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
    sentences = tokenizer.tokenize(text)
    return sentences

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 get_input_sequence(sentence):
    """
    Prepare chatbot's input by tokenizing the sentence and adding necessary punctuation marks.
    Input: "So what's up, buddy"
    Output: ["so", "what", "'", "s", "up", ",", "buddy", ".", "$$$"]
    """
    if not sentence:
        return [START_TOKEN, EOS_SYMBOL]

    # add a dot to the end of the sent in case there is no punctuation mark
    if sentence[-1] not in _PUNKT_MARKS:
        sentence += '.'

    sequence = [START_TOKEN] + tokenize(sentence) + [EOS_SYMBOL]

    return sequence

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 handle_multiple_sentences(infile, outfile):
    titles = []
    f = open(infile, "r")
    f2 = codecs.open(outfile, "w+", "utf-8")
    for line in f:
        line = line.decode("utf-8")
        sentences = sent_detector.tokenize(line.strip())
        for i in range(len(sentences)):
            if i == 0:
                sentences[i] = sentences[i].replace(sentences[i].split()[0],sentences[i].split()[0].title())
            else:
                sentences[i] = sentences[i].replace(sentences[i].split()[0],sentences[i].split()[0].title())
                sentences[i-1] = sentences[i-1].replace(sentences[i-1].split()[-1][-1], " ::::")

        titles.append(" ".join(sentences))
    title_set = set(titles)
    for l in title_set:
        print >> f2, l

def preprocess(s, max_tokens):
    #s = unicode(s, ignore="errors")
    s = s.lower()
    s = re.sub(r'[^\x00-\x7F]+',' ', s)
    s = re.sub("<s>", "", s)
    s = re.sub("<eos>", "", s)
    s = remove_punctuation(s)
    s = re.sub('\d','#',s)
    s = re.sub('\n',' ',s)
    s = re.sub(',',' ',s)

    tokens = WhitespaceTokenizer().tokenize(s)
    #s = replace_the_unfrequent(tokens)
    if (len(tokens) > max_tokens):
    tokens = tokens[:max_tokens]

    s = " ".join(tokens)
    return s, len(tokens)

def analysis(reviews_collection_text):
    with open('data/reviews_%s' % reviews_collection_text, 'r') as f:
        raw_data = f.read()
    with open('data/reviews_%s' % reviews_collection_text, 'r') as f:
        comments = f.readlines()
    data = raw_data.replace('\n', ' ')
    data_lower = data.lower()
    tokens_with_punc = word_tokenize(data_lower)
    tokens = RegexpTokenizer(r'\w+').tokenize(data_lower)
    print("--- Most frequent tokens ---\n",
        FreqDist(tokens_with_punc).most_common(15))
    print("--- Tokens without punctuation ---\n",
        FreqDist(tokens).most_common(15))
    stop = set(stopwords.words('english'))
    words = [word for word in tokens if word not in stop]
    print("--- Most frequent words ---\n", FreqDist(words).most_common(15))
    tagged = pos_tag(words)
    nouns = [word for word, pos in tagged if (pos == 'NN')]
    print("--- Most frequent nouns ---\n", FreqDist(nouns).most_common(15))
    adjts = [word for word, pos in tagged if (pos == 'JJ')]
    print("--- Most frequent adjective ---\n", FreqDist(adjts).most_common(15))
    tokns = [RegexpTokenizer(r'\w+').tokenize(comment) for comment in comments]
    lxdst = [lexical_density(token) for token in tokns if len(token) > 0]
    avgld = sum(lxdst) / len(comments)
    print("--- Average lexical density ---\n", avgld)

def parts_of_speechtag(self, sentences=""):
        from nltk.corpus import state_union  # for importing the already stored data, to be trained with
        from nltk.tokenize import PunktSentenceTokenizer  # importing the already POS intelligent punkbuster tokenizer
        training_text = state_union.raw("2005-GWBUSH.txt")  # Training set imported from the state union local repo.
        sample_text = sentences
        custom_sentence_tokenized = PunktSentenceTokenizer(train_text=training_text)
        # This is the unsupervised learning
        tokenization_unsupervised = custom_sentence_tokenized.tokenize(str(sample_text))

        # tokenizing using unsupervised learning
        # print(tokenization_unsupervised)  # just for hedebuggin purposes
        # print(type(tokenization_unsupervised))  # checking the type of the sentences

        self.processing_POS_tokenization(tokenization_unsupervised=tokenization_unsupervised)

        # Calling the Process content

def __init__(self):
        import nltk
        from nltk.tag import PerceptronTagger
        from nltk.tokenize import TreebankWordTokenizer
        tokenizer_fn = os.path.abspath(resource_filename('phrasemachine.data', 'punkt.english.pickle'))
        tagger_fn = os.path.abspath(resource_filename('phrasemachine.data', 'averaged_perceptron_tagger.pickle'))
        # Load the tagger
        self.tagger = PerceptronTagger(load=False)
        self.tagger.load(tagger_fn)

        # note: nltk.word_tokenize calls the TreebankWordTokenizer, but uses the downloader.
        #       Calling the TreebankWordTokenizer like this allows skipping the downloader.
        #       It seems the TreebankWordTokenizer uses PTB tokenization = regexes. i.e. no downloads
        #       https://github.com/nltk/nltk/blob/develop/nltk/tokenize/treebank.py#L25
        self.tokenize = TreebankWordTokenizer().tokenize
        self.sent_detector = nltk.data.load(tokenizer_fn)


    # http://www.nltk.org/book/ch05.html

def normalize(self, text):
        return [self.stemmer.stem(token) 
                for token in self.tokenizer.tokenize(text.lower()) 
                if token not in self.stop_words]

######### defining a default normalizer ##########

def normalize(self, text):
        return [token for token in self.tokenizer.tokenize(text.lower()) 
                if token not in self.stop_words]

def preprocess(text):
    """
    Preprocess text for encoder
    """
    X = []
    sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
    for t in text:
        sents = sent_detector.tokenize(t)
        result = ''
        for s in sents:
            tokens = word_tokenize(s)
            result += ' ' + ' '.join(tokens)
        X.append(result)
    return X

def preprocess(text):
    """
    Preprocess text for encoder
    """
    X = []
    sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
    for t in text:
        sents = sent_detector.tokenize(t)
        result = ''
        for s in sents:
            tokens = word_tokenize(s)
            result += ' ' + ' '.join(tokens)
        X.append(result)
    return X

def get_tweet_tags(tweet):
    """ Break up a tweet into individual word parts """
    tknzr = TweetTokenizer()
    tokens = tknzr.tokenize(tweet)
    # replace handles with real names
    for n, tok in enumerate(tokens):
        if tok.startswith('@'):
            handle = tok.strip("@")
            if handle in user.students:
                # If we have a database entry for the mentioned user, we can
                # easily substitute a full name.
                usr = user.NPUser(handle)
                tokens[n] = usr.fullname
            else:
                # If there is no database entry, we use the user's alias. While
                # this is the full name in many cases, it is often not reliable
                usr = api.get_user(handle)
                tokens[n] = usr.name
    tagged = nltk.pos_tag(tokens)
    # In nltk, if a teacher's name is written with a period after an
    # abbreviated prefix, it is awkwardly broken up into 3 tags
    for n, tag in enumerate(tagged):
        # If there is the weird period after the prefix,
        if tag[1] == '.':
            # and it is in fact splitting up a person's name,
            if tagged[n - 1][1] == 'NNP' and tagged[n + 1][1] == 'NNP':
                if tagged[n - 1][0] in ['Mr', 'Ms', 'Mrs', 'Mx']:
                    # combine it into the actual name,
                    tagged[n - 1] = ('{}. {}'.format(tagged[n - 1][0],
                                                     tagged[n + 1][0]), 'NNP')
                    # and then remove the extra tags.
                    del tagged[n + 1]
                    del tagged[n]
    return tagged

def tokenize(data):

    sent_tokenize = nltk.tokenize.sent_tokenize

    tokenizer = nltk.tokenize.RegexpTokenizer(u"[\s\.,-?!'\"??\d·•—()׫»%\[\]|?*]+", gaps=True)
    word_tokenize = tokenizer.tokenize


    for text, blockname, textname in data:
        sentences = sent_tokenize(text.strip())
        for sentence in sentences:
            words = word_tokenize(sentence)
            for word in words:
                if len(word) > 1:
                    yield (word, sentence, blockname, textname)

def tokenize(self, text):
        """
           tokenize text into a list of Token objects

            :param text: text to be tokenized (might contains several sentences)
            :type text: str
            :return: List of Token objects
            :rtype: list(Token)
        """
        tokens = []

        if self.tokenizer_type == "SpaceTokenizer":
            operator = RegexpTokenizer('\w+|\$[\d\.]+|\S+')
            for counter, span in enumerate(operator.span_tokenize(text)):
                new_token = Token(counter, text[span[0]:span[1]], span[0], span[1])
                tokens.append(new_token)

        elif self.tokenizer_type == "NLTKWhiteSpaceTokenizer":
            operator = WhitespaceTokenizer()
            for counter, span in enumerate(operator.span_tokenize(text)):
                new_token = Token(counter, text[span[0]:span[1]], span[0], span[1])
                tokens.append(new_token)

        elif self.tokenizer_type == "PTBTokenizer":
            ptb_tokens = word_tokenize(text)
            counter = 0
            for token, span in self._penn_treebank_tokens_with_spans(text, ptb_tokens):
                new_token = Token(counter, token, span[0], span[1])
                counter += 1
                tokens.append(new_token)

        return tokens

def __tokenizeWords(sentence):
    return nltk.tokenize.word_tokenize(sentence)


## tests ########################################################################################

def __init__(self, itemId, questionType, answerType, question, answer, V, WordIDMap):

        self.itemId         = itemId
        self.questionType   = questionType
        self.answerType     = answerType
        self.question       = question
        self.answer         = answer
        self.Question       = [WordIDMap[stemmer.stem(word)] for word in tokenizer.tokenize(question) if stemmer.stem(word) in WordIDMap]
        self.Answer         = [WordIDMap[stemmer.stem(word)] for word in tokenizer.tokenize(answer) if stemmer.stem(word) in WordIDMap]
        self.qFeature       = {}
        self.aFeature       = {}
        self.create_QAFeature()

def __init__(self, itemId, Review, V, WordIDMap, ReviewObj):

        self.itemId     = itemId
        self.sent   = Review
        self.rObj   = ReviewObj
        self.Sent   = [WordIDMap[stemmer.stem(word)] for word in tokenizer.tokenize(Review) if stemmer.stem(word) in WordIDMap]
        self.sFeature   = {}

def preprocess(text):
    """
    Preprocess text for encoder
    """
    X = []
    sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
    for t in text:
        sents = sent_detector.tokenize(t)
        result = ''
        for s in sents:
            tokens = word_tokenize(s)
            result += ' ' + ' '.join(tokens)
        X.append(result)
    return X

def sents(self, fileids=None, categories=None):
        """
        Uses the built in sentence tokenizer to extract sentences from the
        paragraphs. Note that this method uses BeautifulSoup to parse HTML.
        """
        for paragraph in self.paras(fileids, categories):
            for sentence in self._sent_tokenizer.tokenize(paragraph):
                yield sentence

def words(self, fileids=None, categories=None):
        """
        Uses the built in word tokenizer to extract tokens from sentences.
        Note that this method uses BeautifulSoup to parse HTML content.
        """
        for sentence in self.sents(fileids, categories):
            for token in self._word_tokenizer.tokenize(sentence):
                yield token

def describe(self, fileids=None, categories=None):
        """
        Performs a single pass of the corpus and returns a dictionary with a
        variety of metrics concerning the state of the corpus.
        """
        # Structures to perform counting.
        counts  = nltk.FreqDist()
        tokens  = nltk.FreqDist()
        started = time.time()

        # Perform single pass over paragraphs, tokenize and count
        for para in self.paras(fileids, categories):
            counts['paras'] += 1

            for sent in self._sent_tokenizer.tokenize(para):
                counts['sents'] += 1

                for word in self._word_tokenizer.tokenize(sent):
                    counts['words'] += 1
                    tokens[word] += 1

        # Compute the number of files and categories in the corpus
        n_fileids = len(self._resolve(fileids, categories) or self.fileids())
        n_topics  = len(self.categories(self._resolve(fileids, categories)))

        # Return data structure with information
        return {
            'files':  n_fileids,
            'topics': n_topics,
            'paras':  counts['paras'],
            'sents':  counts['sents'],
            'words':  counts['words'],
            'vocab':  len(tokens),
            'lexdiv': float(counts['words']) / float(len(tokens)),
            'ppdoc':  float(counts['paras']) / float(n_fileids),
            'sppar':  float(counts['sents']) / float(counts['paras']),
            'secs':   time.time() - started,
        }

def describe(self, fileids=None, categories=None):
        """
        Performs a single pass of the corpus and returns a dictionary with a
        variety of metrics concerning the state of the corpus.
        """
        # Structures to perform counting.
        counts  = nltk.FreqDist()
        tokens  = nltk.FreqDist()
        started = time.time()

        # Perform single pass over paragraphs, tokenize and count
        for para in self.paras(fileids, categories):
            counts['paras'] += 1

            for sent in para:
                counts['sents'] += 1

                for word, tag in sent:
                    counts['words'] += 1
                    tokens[word] += 1

        # Compute the number of files and categories in the corpus
        n_fileids = len(self._resolve(fileids, categories) or self.fileids())
        n_topics  = len(self.categories(self._resolve(fileids, categories)))

        # Return data structure with information
        return {
            'files':  n_fileids,
            'topics': n_topics,
            'paras':  counts['paras'],
            'sents':  counts['sents'],
            'words':  counts['words'],
            'vocab':  len(tokens),
            'lexdiv': float(counts['words']) / float(len(tokens)),
            'ppdoc':  float(counts['paras']) / float(n_fileids),
            'sppar':  float(counts['sents']) / float(counts['paras']),
            'secs':   time.time() - started,
        }

def stem_and_tokenize_text(text):
    sents = sent_tokenize(text)
    tokens = list(itertools.chain(*[TreebankWordTokenizer().tokenize(sent) for sent in sents]))
    terms = [Term(token) for token in tokens]
    return filter(lambda term: not term.is_punctuation(), terms)

def get_words(text=''):
    words = []
    words = TOKENIZER.tokenize(text)
    filtered_words = []
    for word in words:
        if word in SPECIAL_CHARS or word == " ":
            pass
        else:
            new_word = word.replace(",","").replace(".","")
            new_word = new_word.replace("!","").replace("?","")
            filtered_words.append(new_word)
    return filtered_words

def get_sentences(text=''):
    tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
    sentences = tokenizer.tokenize(text)
    return sentences

def __init__(self, rtepair, stop=True, lemmatize=False):
        """
        :param rtepair: a ``RTEPair`` from which features should be extracted
        :param stop: if ``True``, stopwords are thrown away.
        :type stop: bool
        """
        self.stop = stop
        self.stopwords = set(['a', 'the', 'it', 'they', 'of', 'in', 'to', 'is',
                              'have', 'are', 'were', 'and', 'very', '.', ','])

        self.negwords = set(['no', 'not', 'never', 'failed', 'rejected',
                             'denied'])
        # Try to tokenize so that abbreviations like U.S.and monetary amounts
        # like "$23.00" are kept as tokens.
        from nltk.tokenize import RegexpTokenizer
        tokenizer = RegexpTokenizer('([A-Z]\.)+|\w+|\$[\d\.]+')

        #Get the set of word types for text and hypothesis
        self.text_tokens = tokenizer.tokenize(rtepair.text)
        self.hyp_tokens = tokenizer.tokenize(rtepair.hyp)
        self.text_words = set(self.text_tokens)
        self.hyp_words = set(self.hyp_tokens)

        if lemmatize:
            self.text_words = set(lemmatize(token) for token in self.text_tokens)
            self.hyp_words = set(lemmatize(token) for token in self.hyp_tokens)

        if self.stop:
            self.text_words = self.text_words - self.stopwords
            self.hyp_words = self.hyp_words - self.stopwords

        self._overlap = self.hyp_words & self.text_words
        self._hyp_extra = self.hyp_words - self.text_words
        self._txt_extra = self.text_words - self.hyp_words

def read_block(self, stream):
        block = []
        for para_str in self._para_block_reader(stream):
            para = []
            for sent_str in self._sent_tokenizer.tokenize(para_str):
                sent = self._str2chunktree(sent_str, source_tagset=self._source_tagset,
                                           target_tagset=self._target_tagset)

                # If requested, throw away the tags.
                if not self._tagged:
                    sent = self._untag(sent)

                # If requested, throw away the chunks.
                if not self._chunked:
                    sent = sent.leaves()

                # Add the sentence to `para`.
                if self._group_by_sent:
                    para.append(sent)
                else:
                    para.extend(sent)

            # Add the paragraph to `block`.
            if self._group_by_para:
                block.append(para)
            else:
                block.extend(para)

        # Return the block
        return block

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 tokenize(self, _text):
        if self.regexp:
            return RegexpTokenizer(self.regexp).tokenize(_text)
        return word_tokenize(_text, language=self.language)

def get_words(self, _text):
        return self.tokenize(_text)

def tweet_tokenize(self, tweet):
        #http://www.nltk.org/api/nltk.tokenize.html
        tknzr = TweetTokenizer()
        tokens = tknzr.tokenize(tweet)
        return tokens

def split_sentence_into_words(sentence):
    tokenizer = RegexpTokenizer(r'\w+')
    return tokenizer.tokenize(sentence.lower())

def tokenize(text):
  # lowers = text.lower()
  # no_punctuation = lowers.translate(None, string.punctuation)
  time0 = time.time()
  # tokens = [word[0] for word in TextBlob(unicode(TextBlob(text).correct())).tags if word[1] in ['NN', 'NNS', 'NNP', 'JJ', 'VB'] ]
  # stems = stem_tokens(tokens, stemmer)

  stems = re.findall('[a-z]+', text)
  # stems = [word[0] for word in nltk.pos_tag(tokens) if word[1] in ['NN', 'NNS', 'NNP', 'JJ', 'VB'] ]

  print('%s seconds' % (time.time()-time0))
  print(stems)
  return stems

def tokenize(text):
  # lowers = text.lower()
  # no_punctuation = lowers.translate(None, string.punctuation)
  time0 = time.time()
  # tokens = [word[0] for word in TextBlob(unicode(TextBlob(text).correct())).tags if word[1] in ['NN', 'NNS', 'NNP', 'JJ', 'VB'] ]
  # stems = stem_tokens(tokens, stemmer)

  stems = re.findall('[a-z]+', text)
  # stems = [word[0] for word in nltk.pos_tag(tokens) if word[1] in ['NN', 'NNS', 'NNP', 'JJ', 'VB'] ]

  print('%s seconds' % (time.time()-time0))
  print(stems)
  return stems

def tweet_stemming(tweet, token_freqs):

    """
    Stems tweets words and counts diversty

    :param tweet: the tweet to analyze
    :type tweet: str or unicode

    :param token_freqs: counter of words frequency
    :type token_freqs: Counter

    :returns: words added to token_freqs
    :rtype: int
    """

    pattern_url = '((https?:\/\/)|www\.)([\da-z\.-]+)\.([\/\w \.-]*)( |$)'
    regex_punctuation = re.compile('[%s]' % re.escape(string.punctuation))
    porter = PorterStemmer()

    counter_tokens = 0
    tweet_url_removed = re.sub(pattern_url, '', tweet, flags=re.MULTILINE)  # remove URL
    tweet_url_removed_tokenized = word_tokenize(tweet_url_removed)  # tokenize tweet
    tweet_url_removed_tokenized_cleaned_stemming = []  # cleaned of URLs and hashs, and stemming

    for token in tweet_url_removed_tokenized:
        new_token = regex_punctuation.sub(u'', token)  # remove punctuation and hash
        if not new_token == u'':
            new_token_stemming = porter.stem(new_token)
            tweet_url_removed_tokenized_cleaned_stemming.append(new_token_stemming)
            token_freqs[new_token_stemming] += 1
            counter_tokens += 1

    return counter_tokens

def tokenize(tweet):
    tknzr = TweetTokenizer(strip_handles=True, reduce_len=True, preserve_case=False)
    return tknzr.tokenize(tweet)

# Read cleaned training tweets file into pandas and randomize it

def get_words(text=''):
    words = []
    words = TOKENIZER.tokenize(text)
    filtered_words = []
    for word in words:
        if word in SPECIAL_CHARS or word == " ":
            pass
        else:
            new_word = word.replace(",","").replace(".","")
            new_word = new_word.replace("!","").replace("?","")
            filtered_words.append(new_word)
    return filtered_words

def __init__(self, rtepair, stop=True, lemmatize=False):
        """
        :param rtepair: a ``RTEPair`` from which features should be extracted
        :param stop: if ``True``, stopwords are thrown away.
        :type stop: bool
        """
        self.stop = stop
        self.stopwords = set(['a', 'the', 'it', 'they', 'of', 'in', 'to', 'is',
                              'have', 'are', 'were', 'and', 'very', '.', ','])

        self.negwords = set(['no', 'not', 'never', 'failed', 'rejected',
                             'denied'])
        # Try to tokenize so that abbreviations like U.S.and monetary amounts
        # like "$23.00" are kept as tokens.
        from nltk.tokenize import RegexpTokenizer
        tokenizer = RegexpTokenizer('([A-Z]\.)+|\w+|\$[\d\.]+')

        #Get the set of word types for text and hypothesis
        self.text_tokens = tokenizer.tokenize(rtepair.text)
        self.hyp_tokens = tokenizer.tokenize(rtepair.hyp)
        self.text_words = set(self.text_tokens)
        self.hyp_words = set(self.hyp_tokens)

        if lemmatize:
            self.text_words = set(lemmatize(token) for token in self.text_tokens)
            self.hyp_words = set(lemmatize(token) for token in self.hyp_tokens)

        if self.stop:
            self.text_words = self.text_words - self.stopwords
            self.hyp_words = self.hyp_words - self.stopwords

        self._overlap = self.hyp_words & self.text_words
        self._hyp_extra = self.hyp_words - self.text_words
        self._txt_extra = self.text_words - self.hyp_words

def read_block(self, stream):
        block = []
        for para_str in self._para_block_reader(stream):
            para = []
            for sent_str in self._sent_tokenizer.tokenize(para_str):
                sent = self._str2chunktree(sent_str, source_tagset=self._source_tagset,
                                           target_tagset=self._target_tagset)

                # If requested, throw away the tags.
                if not self._tagged:
                    sent = self._untag(sent)

                # If requested, throw away the chunks.
                if not self._chunked:
                    sent = sent.leaves()

                # Add the sentence to `para`.
                if self._group_by_sent:
                    para.append(sent)
                else:
                    para.extend(sent)

            # Add the paragraph to `block`.
            if self._group_by_para:
                block.append(para)
            else:
                block.extend(para)

        # Return the block
        return block

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'])