def text_to_wordlist(text, remove_stopwords=False, stem_words=False):
# Clean the text, with the option to remove stopwords and to stem words.
# Convert words to lower case and split them
# Regex to remove all Non-Alpha Numeric and space
special_character_removal = re.compile(r'[^a-z\d ]', re.IGNORECASE)
# regex to replace all numerics
replace_numbers = re.compile(r'\d+', re.IGNORECASE)
# text = text.lower().split()
text = text.split()
# Optionally, remove stop words
if remove_stopwords:
stops = set(stopwords.words("english"))
text = [w for w in text if not w in stops]
text = " ".join(text)
# Remove Special Characters
text = special_character_removal.sub('', text)
# Replace Numbers
text = replace_numbers.sub('n', text)
# Optionally, shorten words to their stems
if stem_words:
text = text.split()
stemmer = SnowballStemmer('english')
stemmed_words = [stemmer.stem(word) for word in text]
text = " ".join(stemmed_words)
# Return a list of words
return (text)
def stopwords_remover(text):
text = re.sub('\'\w+', '', text) # Remove ticks and the next character
text = ' '.join(word for word in text.split()
if word not in STOPWORDS) # remove stopwors from text
text = ' '.join(word for word in text.split()
if len(word) > 3) # remove stopwors from text
return text
def process_sentence(text, objects='False'):
'''
Simple and dirty text preprocessing to remove some mispelled words
and lemmatize
'''
text = text.lower()
old_text = text
text = text.replace('1', 'one').replace('2','two').replace(
'3','three').replace('4','four').replace('5','five').replace('6','six').replace(
'.','').replace('contains', 'contain').replace(
'which','').replace('are there','there are').replace(
'there is', '').replace('ablue', 'a blue').replace(
'corner','edge').replace('wall', 'edge').replace('yelow', 'yellow').replace(
'below','beneath').replace(
'brick','block').replace('leats','least').replace('is touching', 'touching')
text = re.sub(r'colour([\W])', 'color ', text)
text = re.sub(r'colored([\W])', 'color ', text)
text = re.sub(r'coloured([\W])', 'color ', text)
text = text.split(' ')
text = map(correction, [t for t in text if t])
text = [lemmatizer.lemmatize(x) if not x in [u'as',u'wall'] else x for x in text]
text = ' '.join(text)
if 'that' in text:
text = text.replace('that', '')
if 'contain' in text or 'ha ' in text:
text = text.replace('contain', 'with').replace('ha ','with ')
text = re.sub(r'(^|\W)a([\W])', ' one ', text)
text = re.sub(r'(^)ll ', ' ', text)
text = re.sub(r'(^)t ', 'at ', text)
text = ' '.join([t for t in text.split(' ') if t])
text = text.replace('based', 'base')
return text
def text_to_wordlist(text, remove_stopwords=False, stem_words=False,comma=True):
# Clean the text, with the option to remove stopwords and to stem words.
import re
# Convert words to lower case and split them
text = text.lower().split()
# Optionally, remove stop words
if remove_stopwords:
stops = set(stopwords.words("english"))
text = [w for w in text if not w in stops]
text = " ".join(text)
# Clean the text
if not comma:
text = re.sub(r"[,:\/\^.$%#+-></\?\=*\\]", " ", text) # origin is [^A-Za-z0-9^,!.\/'+-=?]
else:
pass
# Optionally, shorten words to their stems
if stem_words:
text = text.split()
stemmer = SnowballStemmer('english')
stemmed_words = [stemmer.stem(word) for word in text]
text = " ".join(stemmed_words)
# Return a list of words
return text
def contract(text):
text = [wn.lemmatize(word) for word in text]
text = [ps.stem(word) for word in text]
text = "".join([char for char in text if char not in string.punctuation])
text = ' '.join(word for word in text.split()
if word not in stopword) # delete stopwors from text
return text #Write function to conduct all text contraction
def neg_emo(text):
words = text.split(" ")
sc = 0
for word in words:
if word.lower() in neg_e:
sc += 1
return sc
def clean_text(text):
text = text.lower()
text = REPLACE_BY_SPACE.sub(' ', text)
text = BAD_SYMBOLS.sub(' ', text)
text = re.sub(r"\'s", " ", text)
text = ' '.join(word for word in text.split() if word not in STOPWORDS)
return text
def cleanText(text):
# Replace non-ASCII characters with printable ASCII.
# Use HTML entities when possible
if None == text:
return ''
text = re.sub(r'\x85', '…', text) # replace ellipses
text = re.sub(r'\x91', "‘", text) # replace left single quote
text = re.sub(r'\x92', "’", text) # replace right single quote
text = re.sub(r'\x93', '“', text) # replace left double quote
text = re.sub(r'\x94', '”', text) # replace right double quote
text = re.sub(r'\x95', '•', text) # replace bullet
text = re.sub(r'\x96', '-', text) # replace bullet
text = re.sub(r'\x99', '™', text) # replace TM
text = re.sub(r'\xae', '®', text) # replace (R)
text = re.sub(r'\xb0', '°', text) # replace degree symbol
text = re.sub(r'\xba', '°', text) # replace degree symbol
# Do you want to keep new lines / carriage returns? These are generally
# okay and useful for readability
text = re.sub(r'[\n\r\t]+', ' ', text) # remove embedded \n and \r
#removes numbers
text = re.sub(" \d+", " ", text)
# This is a hard-core line that strips everything else.
text = re.sub(r'[\x00-\x1f\x80-\xff]', ' ', text)
stop_words = set(stopwords.words('english'))
text = ' '.join([word for word in text.split() if word not in stop_words])
# tagged_sentence = nltk.tag.pos_tag(text.split())
# text = ' '.join([word for word,tag in tagged_sentence if tag != 'NNP' and tag != 'NNPS' and word != stop_words])
return text
def get_high_frequency_word(textlist):
dic = {}
for text in textlist:
splittest = text.split(" ")
for word in splittest:
if (word == ""
): # spilt 后需要清洗==============================================
continue
if dic:
flag = False
for dicword in dic:
if (word == dicword):
flag = True
dic[word] += 1
break
if (flag == False):
dic[word] = 1
else:
dic[word] = 1
sorted_dict = sorted(dic.items(), key=lambda x: x[1], reverse=True)
# print(sorted_dict) # 倒序排列词频 打印-------------------------------------
# print(len(sorted_dict))
# print("---------------------------------------------")
# low_frequency_word =[]
high_frequency_word = []
count = 0
for item in sorted_dict:
if (item[1] > 20):
count += 1
high_frequency_word.append(item[0])
count *= 0.7
count = numpy.math.ceil(count)
return high_frequency_word[:count]
def text_to_word_sequence(text,
filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
lower=True, split=" "):
"""Converts a text to a sequence of words (or tokens).
# Arguments
text: Input text (string).
filters: Sequence of characters to filter out.
lower: Whether to convert the input to lowercase.
split: Sentence split marker (string).
# Returns
A list of words (or tokens).
"""
if lower:
text = text.lower()
if sys.version_info < (3,) and isinstance(text, unicode):
translate_map = dict((ord(c), unicode(split)) for c in filters)
else:
translate_map = maketrans(filters, split * len(filters))
text = text.translate(translate_map)
#seq = text.split(split)
seq = text.split()
#seq = word_tokenize(text)
#print("text:",seq)
#pos_seq = nltk.pos_tag(text)
#return [i for i in seq if i]
return nltk.pos_tag(seq)
def pos_emo(text):
words = text.split(" ")
sc = 0
for word in words:
if word.lower() in pos_e:
sc = 1
return sc
def statistics_unique_words(text):
words_set = set()
for token in text.split():
words_set.add(token)
return len(words_set)
def clean_contractions(text, mapping):
specials = ["’", "‘", "´", "`"]
for s in specials:
text = text.replace(s, "'")
text = ' '.join(
[mapping[t] if t in mapping else t for t in text.split(" ")])
return text
def swear_number(text):
words = text.split(" ")
sc = 0
for word in words:
if word.lower() in sl:
sc = +1
return sc
def apply_cleaning_function(self, fn, texts, description=""):
result = [fn(text) for text in texts]
sentences = [text.split() for text in result]
tf_dict = self.build_tf_dict(sentences)
oov = self.check_coverage(tf_dict)
# print(oov[:10])
return result
def text_to_data(self, text, author):
#remove newlines, numbers, some punctuation
text = text.replace('\n', " ")
text = re.sub(r'[0-9]+', '', text)
sent_tokenize_list = sent_tokenize(text)
total_arr = [(x, author) for x in sent_tokenize_list]
vocab_count = len(set(text.split(' ')))
return total_arr, vocab_count
def percent(text):
count = 0
text = "".join(c for c in text if c not in ('!','.',':','?',';'))
words = text.split()
for word in words:
if word.lower() in stop_words:
count +=1
return count / len(words)
def text_to_wordlist(text, remove_stopwords=False, stem_words=False):
# Clean the text, with the option to remove stopwords and to stem words.
# Convert words to lower case and split them
wiki_reg = r'https?://en.wikipedia.org/[-A-Za-z0-9+&@#/%?=~_|!:,.;]+[-A-Za-z0-9+&@#/%=~_|]'
url_reg = r'https?://[-A-Za-z0-9+&@#/%?=~_|!:,.;]+[-A-Za-z0-9+&@#/%=~_|]'
ip_reg = '\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}'
WIKI_LINK = ' WIKI_LINK '
URL_LINK = ' URL_LINK '
IP_LINK = ' IP_LINK '
#clear link
c = re.findall(wiki_reg, text)
for u in c:
text = text.replace(u, WIKI_LINK)
c = re.findall(url_reg, text)
for u in c:
text = text.replace(u, WIKI_LINK)
c = re.findall(wiki_reg, text)
for u in c:
text = text.replace(u, URL_LINK)
c = re.findall(ip_reg, text)
# Regex to remove all Non-Alpha Numeric and space
special_character_removal = re.compile(r'[^A-Za-z\d!?*\' ]', re.IGNORECASE)
# regex to replace all numerics
replace_numbers = re.compile(r'\d+', re.IGNORECASE)
# text = text.lower().split()
text = text.split()
# Optionally, remove stop words
if remove_stopwords:
stops = set(stopwords.words("english"))
text = [w for w in text if not w in stops]
text = " ".join(text)
# Remove Special Characters
text = special_character_removal.sub('', text)
# Replace Numbers
text = replace_numbers.sub('NUMBERREPLACER', text)
# Optionally, shorten words to their stems
if stem_words:
text = text.split()
stemmer = SnowballStemmer('english')
stemmed_words = [stemmer.stem(word) for word in text]
text = " ".join(stemmed_words)
# Return a list of words
return (text)
def text_to_wordlist(text, remove_stopwords=True, stem_words=False):
# Clean the text, with the option to remove stopwords and to stem words.
# Convert words to lower case and split them
text = text.lower()
# Clean the text
text = re.sub(r"[^A-Za-z0-9']", " ", text)
text = re.sub(r"what's", "", text)
text = re.sub(r"\'s", " ", text)
text = re.sub(r"\'ve", " have ", text)
text = re.sub(r"can't", "cannot ", text)
text = re.sub(r"n't", " not ", text)
text = re.sub(r"i'm", "i am", text)
text = re.sub(r" m ", " am ", text)
text = re.sub(r"\'re", " are ", text)
text = re.sub(r"\'d", " would ", text)
text = re.sub(r"\'ll", " will ", text)
text = re.sub(r"(\d+)(k)", r"\g<1>000", text)
text = re.sub(r" e g ", " eg ", text)
text = re.sub(r" b g ", " bg ", text)
text = re.sub(r"\0s", "0", text)
text = re.sub(r" 9 11 ", "911", text)
text = re.sub(r"e-mail", "email", text)
text = re.sub(r"\s{2,}", " ", text)
text = re.sub(r"quikly", "quickly", text)
text = re.sub(r" usa ", " america ", text)
text = re.sub(r" u s ", " america ", text)
text = re.sub(r" uk ", " england ", text)
text = re.sub(r"imrovement", "improvement", text)
text = re.sub(r"intially", "initially", text)
text = re.sub(r" dms ", "direct messages ", text)
text = re.sub(r"demonitization", "demonetization", text)
text = re.sub(r"actived", "active", text)
text = re.sub(r"kms", " kilometers ", text)
text = re.sub(r" cs ", " computer science ", text)
text = re.sub(r" upvotes ", " up votes ", text)
text = re.sub(r" iphone ", " phone ", text)
text = re.sub(r"\0rs ", " rs ", text)
text = re.sub(r"calender", "calendar", text)
text = re.sub(r"ios", "operating system", text)
text = re.sub(r"programing", "programming", text)
text = re.sub(r"bestfriend", "best friend", text)
text = re.sub(r"iii", "3", text)
text = re.sub(r"the us", "america", text)
text = re.sub(r" j k ", " jk ", text)
# Optionally, shorten words to their stems
if stem_words:
text = text.split()
stemmer = SnowballStemmer('english')
stemmed_words = [stemmer.stem(word) for word in text]
text = " ".join(stemmed_words)
# Return a list of words
return (text)
def _remove_pattern_2(input_text_list):
stoplist = read_stopwords()
cleaned_text_list = []
for text in input_text_list:
text = text.translate(string.punctuation) # Remove puncuation 去除标点
text = text.lower() # Convert words to lower case and split them
# text = " ".join(text)
# Clean the text
text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ",
text) # 除A-Za-z0-9(),!?'`外的字符，去除
text = re.sub(r"what's", "what is ", text)
text = re.sub(r"\'s", " ", text)
text = re.sub(r"\'ve", " have ", text)
text = re.sub(r"n't", " not ", text)
text = re.sub(r"i'm", "i am ", text)
text = re.sub(r"\'re", " are ", text)
text = re.sub(r"\'d", " would ", text)
text = re.sub(r"\'ll", " will ", text)
text = re.sub(r",", " ", text)
text = re.sub(r"\.", " ", text)
text = re.sub(r"!", " ! ", text)
text = re.sub(r"\/", " ", text)
text = re.sub(r"\^", " ^ ", text)
text = re.sub(r"\+", " + ", text)
text = re.sub(r"\-", " - ", text)
text = re.sub(r"\=", " = ", text)
text = re.sub(r"'", " ", text)
text = re.sub(r"(\d+)(k)", r"\g<1>000", text)
text = re.sub(r":", " : ", text)
text = re.sub(r" e g ", " eg ", text)
text = re.sub(r" b g ", " bg ", text)
text = re.sub(r" u s ", " american ", text)
text = re.sub(r"\0s", "0", text)
text = re.sub(r" 9 11 ", "911", text)
text = re.sub(r"e - mail", "email", text)
text = re.sub(r"j k", "jk", text)
text = re.sub(r"\s{2,}", " ", text)
text = re.sub(r"https://t.co/[A-Za-z]{10}", " ", text)
text = text.split()
text = [word for word in text
if word not in stoplist] ## 在提取词根前清除一次停用词
stemmer = SnowballStemmer('english')
stemmed_words = [stemmer.stem(word) for word in text]
cleanwordlist = [
word for word in stemmed_words if word not in stoplist
] ## 提取词根后，再清除
text = " ".join(cleanwordlist)
cleaned_text_list.append(text)
return cleaned_text_list
def negations(text):
replacer = AntonymReplacer()
sent = text.split()
noneg = replacer.replace_negations(sent)
separator = ' '
out = separator.join(noneg)
return out
def bigram_text_to_word_sequence(text, bigram, filters=base_filter(), lower=False, split=" "):
'''prune: sequence of characters to filter out
'''
if lower:
text = text.lower()
text = text.translate(string.maketrans(filters, split*len(filters)))
seq = text.split(split)
sentences = [_f for _f in seq if _f]
return bigram(sentences)
def inputpreprocess(text):
t = ' '.join([
t for t in text.split() if t not in russian_stop
and t not in punctuations and '\n' != t and " " != t
])
t = [s for s in t if "\n" != s and '"' != s]
text = "".join(t)
text = re.sub(r"^\s+", "", text)
return text
def remove_stopwords(text):
stop = set(stopwords.words('english'))
punctuation = list(string.punctuation)
stop.update(punctuation)
final_text = []
for i in text.split():
if i.strip().lower() not in stop:
final_text.append(i.strip())
return " ".join(final_text)
def text_to_word_sequence(text,
filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
lower=True, split=" "):
if lower: text = text.lower()
if type(text) == unicode:
translate_table = {ord(c): ord(t) for c, t in zip(filters, split * len(filters))}
else:
translate_table = maketrans(filters, split * len(filters))
text = text.translate(translate_table)
seq = text.split(split)
return [i for i in seq if i]
def clean_text(text):
"""
text: a string
return: modified initial string
"""
text = text.lower() # lowercase text
text = REPLACE_BY_SPACE_RE.sub(' ', text) # replace REPLACE_BY_SPACE_RE symbols by space in text
#text = BAD_SYMBOLS_RE.sub('', text) # delete symbols which are in BAD_SYMBOLS_RE from text
text = ' '.join(word for word in text.split() if word not in stopwords) # delete stopwors from text
return text
def removeStopWords(sen):
text = re.sub('[^a-zA-z&]', ' ', sen)
text = text.lower()
text = text.split()
ps = PorterStemmer()
text = [
ps.stem(word) for word in text
if not word in set(stopwords.words('english'))
]
text = ' '.join(text)
return text
def clean_contractions(text, mapping):
'''
credits to: https://www.kaggle.com/christofhenkel/how-to-preprocessing-when-using-embeddings
credits to: https://www.kaggle.com/anebzt/quora-preprocessing-model
input: current text, contraction mappings
output: modify the comments to use the base form from contraction mapping
'''
specials = ["’", "‘", "´", "`"]
for s in specials:
text = text.replace(s, "'")
text = ' '.join([mapping[t] if t in mapping else t for t in text.split(" ")])
return text
def text_to_wordlist(text, remove_stopwords=False, stem_words=False):
# Clean the text, with the option to remove stopwords and to stem words.
# Convert words to lower case and split them
text = text.lower().split()
# Optionally, remove stop words
if remove_stopwords:
stops = set(stopwords.words("english"))
text = [w for w in text if not w in stops]
text = " ".join(text)
# Clean the text
text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ", text)
text = re.sub(r"what's", "what is ", text)
text = re.sub(r"\'s", " ", text)
text = re.sub(r"\'ve", " have ", text)
text = re.sub(r"can't", "cannot ", text)
text = re.sub(r"n't", " not ", text)
text = re.sub(r"i'm", "i am ", text)
text = re.sub(r"\'re", " are ", text)
text = re.sub(r"\'d", " would ", text)
text = re.sub(r"\'ll", " will ", text)
text = re.sub(r",", " ", text)
text = re.sub(r"\.", " ", text)
text = re.sub(r"!", " ! ", text)
text = re.sub(r"\/", " ", text)
text = re.sub(r"\^", " ^ ", text)
text = re.sub(r"\+", " + ", text)
text = re.sub(r"\-", " - ", text)
text = re.sub(r"\=", " = ", text)
text = re.sub(r"'", " ", text)
text = re.sub(r"(\d+)(k)", r"\g<1>000", text)
text = re.sub(r":", " : ", text)
text = re.sub(r" e g ", " eg ", text)
text = re.sub(r" b g ", " bg ", text)
text = re.sub(r" u s ", " american ", text)
text = re.sub(r"\0s", "0", text)
text = re.sub(r" 9 11 ", "911", text)
text = re.sub(r"e ?-? ?mail", "email", text)
text = re.sub(r"j k", "jk", text)
text = re.sub(r"\s{2,}", " ", text)
# Optionally, shorten words to their stems
if stem_words:
text = text.split()
stemmer = SnowballStemmer('english')
stemmed_words = [stemmer.stem(word) for word in text]
text = " ".join(stemmed_words)
# Return a list of words
return (text)
def clean_special_chars(text, punct, mapping):
'''
credits to: https://www.kaggle.com/christofhenkel/how-to-preprocessing-when-using-embeddings
credits to: https://www.kaggle.com/anebzt/quora-preprocessing-model
input: current text, punctuations, punctuation mapping
output: cleaned text
'''
for p in punct:
text = text.replace(p, " ")
text = ' '.join(text.split())
for p in mapping:
text = text.replace(p, mapping[p])
return text