def preprocess_sentence(sent, lower=True):
"""Pre-process a sentence ( via ``textacy.preprocess` module ).
Args:
sent (str): text.
lower (bool): whether to return a lowercase string.
Returns:
str
"""
# normalize unicode
sent = preprocessing.normalize_unicode(sent)
# deaccent
sent = preprocessing.remove_accents(sent)
# replace newline chars
sent = re.sub("\n|\r", " ", sent)
# unpack contractions
sent = contractions.fix(sent)
# replace emoji symbols
sent = preprocessing.replace_emojis(sent)
# replace hashtags
sent = preprocessing.replace_hashtags(sent)
# replace user handles
sent = preprocessing.replace_user_handles(sent)
# replace currency symbols
sent = preprocessing.replace_currency_symbols(sent)
# replace emails
sent = preprocessing.replace_emails(sent)
# replace URLs
sent = preprocessing.replace_urls(sent)
# remove punctuation
sent = preprocessing.remove_punctuation(sent)
# normalize whitespace
sent = preprocessing.normalize_whitespace(sent)
if lower:
sent = sent.lower()
return sent
def test_plaintext_functionality(text):
preprocessed_text = preprocessing.normalize_whitespace(text)
preprocessed_text = preprocessing.remove_punctuation(text)
preprocessed_text = preprocessed_text.lower()
assert all(char.islower() for char in preprocessed_text if char.isalpha())
assert all(char.isalnum() or char.isspace() for char in preprocessed_text)
keyword = "America"
kwics = text_utils.keyword_in_context(text,
keyword,
window_width=35,
print_only=False)
for pre, kw, post in kwics:
assert kw == keyword
assert isinstance(pre, compat.unicode_)
assert isinstance(post, compat.unicode_)
def text_cleanup(text):
"cleanup our text"
text = preprocessing.replace_emails(text, replace_with='')
text = preprocessing.replace_urls(text, replace_with='')
text = preprocessing.replace_hashtags(text, replace_with='')
text = preprocessing.replace_phone_numbers(text, replace_with='')
text = preprocessing.replace_numbers(text, replace_with='')
text = preprocessing.remove_accents(text)
text = preprocessing.remove_punctuation(text)
text = preprocessing.normalize_quotation_marks(text)
text = preprocessing.normalize_hyphenated_words(text)
text = text.replace('\n', ' ').replace('\t', ' ')
text = text.lower()
text = preprocessing.normalize_whitespace(text)
return text
def textacy_preprocess(sentence):
"""Preprocess text."""
sentence = preprocessing.normalize_hyphenated_words(sentence)
sentence = preprocessing.normalize_quotation_marks(sentence)
#sentence = preprocessing.normalize_repeating_chars(sentence)
sentence = preprocessing.normalize_unicode(sentence)
sentence = preprocessing.normalize_whitespace(sentence)
sentence = preprocessing.remove_accents(sentence)
sentence = preprocessing.remove_punctuation(sentence)
sentence = preprocessing.replace_currency_symbols(sentence)
sentence = preprocessing.replace_emails(sentence)
sentence = preprocessing.replace_emojis(sentence)
sentence = preprocessing.replace_hashtags(sentence)
sentence = preprocessing.replace_numbers(sentence)
sentence = preprocessing.replace_phone_numbers(sentence)
sentence = preprocessing.replace_urls(sentence)
sentence = preprocessing.replace_user_handles(sentence)
return sentence
def load(path):
email_text = extract_email_text(path)
if not email_text:
return []
# use textacy to do the processing, remove the whitesapace, punctuation
email_text = preprocessing.normalize_whitespace(
preprocessing.remove_punctuation(email_text))
# remove accents and noralize unicode
email_text = preprocessing.normalize_unicode(
preprocessing.remove_accents(email_text))
# Tokenize the message
tokens = to_tokenized_text(email_text)
# Remove stopwords and stem tokens
if len(tokens) > 2:
# extract stemming word
return [w.lemma_ for w in tokens if w not in nlp.Defaults.stopwords]
return []
def test_remove_punct():
text = "I can't. No, I won't! It's a matter of \"principle\"; of -- what's the word? -- conscience."
proc_text = "I can t No I won t It s a matter of principle of what s the word conscience "
assert preprocessing.remove_punctuation(text) == proc_text
def test_remove_punct_marks():
text = "I can't. No, I won't! It's a matter of \"principle\"; of -- what's the word? -- conscience."
proc_text = "I can t. No, I won t! It s a matter of principle ; of what s the word? conscience."
assert preprocessing.remove_punctuation(text, marks="-'\"") == proc_text
def preprocess_text(text,
char_count_filter=True,
stopwords=None,
min_len=2,
max_len=15):
"""
Pre-processing steps prior to spaCy nlp pipeline. Optional filtering of
tokens based on character length.
Parameters
----------
text : str
char_count_filter : bool
stopwords : iterable, None
min_len : int
max_len : int
Returns
-------
text : str
pre-processed text
"""
# 1) convert to lower case for robust stop-word recognition
text = text.lower()
# 2) normalise
text = preprocessing.normalize_quotation_marks(text)
# text = preprocessing.normalize_repeating_chars(text)
text = preprocessing.normalize_hyphenated_words(text)
text = preprocessing.normalize_whitespace(text)
# 3) replace
text = preprocessing.replace_currency_symbols(text)
text = preprocessing.replace_emails(text)
text = preprocessing.replace_emojis(text)
text = preprocessing.replace_hashtags(text)
text = preprocessing.replace_numbers(text)
text = preprocessing.replace_phone_numbers(text)
text = preprocessing.replace_urls(text)
text = preprocessing.replace_user_handles(text)
# 4) remove
text = preprocessing.remove_accents(text)
text = preprocessing.remove_punctuation(text)
text = re.sub("[^A-Za-z0-9]+", " ", text) # keep text and numbers
# 5) optionally remove tokens based on length
if char_count_filter & (stopwords is not None):
# filter based on token length
tokens = gensim.utils.simple_preprocess(doc=text,
min_len=min_len,
max_len=max_len)
# filter case-specific words
tokens = [token for token in tokens if token not in stopwords]
# convert processed list of tokens back to one string
text = " ".join(tokens)
else:
raise NotImplementedError("Not implemented.")
return text
def clean_tweet(self, text):
# FIXED UNICODE
# text = preprocess.fix_bad_unicode(text)
text = ftfy.fix_text(text)
# GET TEXT ONLY FROM HTML
text = BeautifulSoup(text, features='lxml').getText()
# UN-PACK CONTRACTIONS
text = preprocess.unpack_contractions(text)
# REMOVE URL
# text = preprocess.replace_urls(text)
text = preprocessing.replace_urls(text)
# REMOVE EMAILS
# text = preprocess.replace_emails(text)
text = preprocessing.replace_emails(text)
# REMOVE PHONE NUMBERS
# text = preprocess.replace_phone_numbers(text)
text = preprocessing.replace_phone_numbers(text)
# REMOVE NUMBERS
# text = preprocess.replace_numbers(text)
text = preprocessing.replace_numbers(text)
# REMOVE CURRENCY
# text = preprocess.replace_currency_symbols(text)
text = preprocessing.replace_currency_symbols(text)
# REMOVE ACCENTS
# text = preprocess.remove_accents(text)
text = preprocessing.remove_accents(text)
# CONVERT EMOJIS TO TEXT
words = text.split()
reformed = [
self.SMILEY[word] if word in self.SMILEY else word
for word in words
]
text = " ".join(reformed)
text = emoji.demojize(text)
text = text.replace(":", " ")
text = ' '.join(text.split())
# SPLIT ATTACHED WORDS
text = ' '.join(re.findall('[A-Z][^A-Z]*', text))
# SPLIT UNDERSCORE WORDS
text = text.replace('_', ' ')
# REMOVE PUNCTUATION
# text = preprocess.remove_punct(text)
text = preprocessing.remove_punctuation(text)
# Remove numbers
text = re.sub(r'\d', '', text)
# REMOVE WORDS LESS THAN 3 CHARACTERS
text = re.sub(r'\b\w{1,2}\b', '', text)
# NORMALIZE WHITESPACE
# text = preprocess.normalize_whitespace(text)
text = preprocessing.normalize_whitespace(text)
return text
def preprocess(text):
return preprocessing.normalize_whitespace(preprocessing.remove_punctuation(text))
binance_words = nlp(binance_string)._.combo_basic.sort_values(ascending=False).head(1000)
# In[ ]:
binance3000.text.to_csv('binance3000_texts.csv')
# In[ ]:
# %%
from textacy import preprocessing
df3 = preprocessing.normalize_whitespace(preprocessing.remove_punctuation(df3.text))
# %%
import textacy
textacy.text_utils.KWIC(strings, "language", window_width=35)
# %%
# %%