How to use the contractions.CONTRACTION_MAP function in contractions

def normalize_corpus(corpus, lemmatize=True, 
                     only_text_chars=False,
                     tokenize=False):
    
    normalized_corpus = []    
    for text in corpus:
        text = html_parser.unescape(text)
        text = expand_contractions(text, CONTRACTION_MAP)
        if lemmatize:
            text = lemmatize_text(text)
        else:
            text = text.lower()
        text = remove_special_characters(text)
        text = remove_stopwords(text)
        if only_text_chars:
            text = keep_text_characters(text)
        
        if tokenize:
            text = tokenize_text(text)
            normalized_corpus.append(text)
        else:
            normalized_corpus.append(text)
            
    return normalized_corpus

def normalize_corpus(corpus, lemmatize=True, 
                     only_text_chars=False,
                     tokenize=False):
    
    normalized_corpus = []    
    for index, text in enumerate(corpus):
        text = normalize_accented_characters(text)
        text = html_parser.unescape(text)
        text = strip_html(text)
        text = expand_contractions(text, CONTRACTION_MAP)
        if lemmatize:
            text = lemmatize_text(text)
        else:
            text = text.lower()
        text = remove_special_characters(text)
        text = remove_stopwords(text)
        if only_text_chars:
            text = keep_text_characters(text)
        
        if tokenize:
            text = tokenize_text(text)
            normalized_corpus.append(text)
        else:
            normalized_corpus.append(text)
            
    return normalized_corpus

def normalize_corpus(corpus, tokenize=False):
    
    normalized_corpus = []    
    for text in corpus:
        text = expand_contractions(text, CONTRACTION_MAP)
        text = lemmatize_text(text)
        text = remove_special_characters(text)
        text = remove_stopwords(text)
        normalized_corpus.append(text)
        if tokenize:
            text = tokenize_text(text)
            normalized_corpus.append(text)
            
    return normalized_corpus

contractions_pattern = re.compile('({})'.format('|'.join(contraction_mapping.keys())), 
                                      flags=re.IGNORECASE|re.DOTALL)
    def expand_match(contraction):
        match = contraction.group(0)
        first_char = match[0]
        expanded_contraction = contraction_mapping.get(match)\
                                if contraction_mapping.get(match)\
                                else contraction_mapping.get(match.lower())                       
        expanded_contraction = first_char+expanded_contraction[1:]
        return expanded_contraction
        
    expanded_sentence = contractions_pattern.sub(expand_match, sentence)
    return expanded_sentence
    
expanded_corpus = [expand_contractions(sentence, CONTRACTION_MAP) 
                    for sentence in cleaned_corpus]    
print expanded_corpus
print 

    
# case conversion    
print corpus[0].lower()
print corpus[0].upper()
 
       
# removing stopwords
def remove_stopwords(tokens):
    stopword_list = nltk.corpus.stopwords.words('english')
    filtered_tokens = [token for token in tokens if token not in stopword_list]
    return filtered_tokens

def normalize_corpus(corpus, lemmatize=True, tokenize=False):
    
    normalized_corpus = []  
    for text in corpus:
        text = html_parser.unescape(text)
        text = expand_contractions(text, CONTRACTION_MAP)
        if lemmatize:
            text = lemmatize_text(text)
        else:
            text = text.lower()
        text = remove_special_characters(text)
        text = remove_stopwords(text)
        if tokenize:
            text = tokenize_text(text)
            normalized_corpus.append(text)
        else:
            normalized_corpus.append(text)
            
    return normalized_corpus