You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
345 lines
11 KiB
Python
345 lines
11 KiB
Python
# coding: utf-8
|
|
#
|
|
# Natural Language Toolkit: Twitter Tokenizer
|
|
#
|
|
# Copyright (C) 2001-2020 NLTK Project
|
|
# Author: Christopher Potts <cgpotts@stanford.edu>
|
|
# Ewan Klein <ewan@inf.ed.ac.uk> (modifications)
|
|
# Pierpaolo Pantone <> (modifications)
|
|
# URL: <http://nltk.org/>
|
|
# For license information, see LICENSE.TXT
|
|
#
|
|
|
|
|
|
"""
|
|
Twitter-aware tokenizer, designed to be flexible and easy to adapt to new
|
|
domains and tasks. The basic logic is this:
|
|
|
|
1. The tuple regex_strings defines a list of regular expression
|
|
strings.
|
|
|
|
2. The regex_strings strings are put, in order, into a compiled
|
|
regular expression object called word_re.
|
|
|
|
3. The tokenization is done by word_re.findall(s), where s is the
|
|
user-supplied string, inside the tokenize() method of the class
|
|
Tokenizer.
|
|
|
|
4. When instantiating Tokenizer objects, there is a single option:
|
|
preserve_case. By default, it is set to True. If it is set to
|
|
False, then the tokenizer will downcase everything except for
|
|
emoticons.
|
|
|
|
"""
|
|
|
|
|
|
######################################################################
|
|
|
|
import regex # https://github.com/nltk/nltk/issues/2409
|
|
import html
|
|
|
|
######################################################################
|
|
# The following strings are components in the regular expression
|
|
# that is used for tokenizing. It's important that phone_number
|
|
# appears first in the final regex (since it can contain whitespace).
|
|
# It also could matter that tags comes after emoticons, due to the
|
|
# possibility of having text like
|
|
#
|
|
# <:| and some text >:)
|
|
#
|
|
# Most importantly, the final element should always be last, since it
|
|
# does a last ditch whitespace-based tokenization of whatever is left.
|
|
|
|
# ToDo: Update with http://en.wikipedia.org/wiki/List_of_emoticons ?
|
|
|
|
# This particular element is used in a couple ways, so we define it
|
|
# with a name:
|
|
EMOTICONS = r"""
|
|
(?:
|
|
[<>]?
|
|
[:;=8] # eyes
|
|
[\-o\*\']? # optional nose
|
|
[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
|
|
|
|
|
[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
|
|
[\-o\*\']? # optional nose
|
|
[:;=8] # eyes
|
|
[<>]?
|
|
|
|
|
<3 # heart
|
|
)"""
|
|
|
|
# URL pattern due to John Gruber, modified by Tom Winzig. See
|
|
# https://gist.github.com/winzig/8894715
|
|
|
|
URLS = r""" # Capture 1: entire matched URL
|
|
(?:
|
|
https?: # URL protocol and colon
|
|
(?:
|
|
/{1,3} # 1-3 slashes
|
|
| # or
|
|
[a-z0-9%] # Single letter or digit or '%'
|
|
# (Trying not to match e.g. "URI::Escape")
|
|
)
|
|
| # or
|
|
# looks like domain name followed by a slash:
|
|
[a-z0-9.\-]+[.]
|
|
(?:[a-z]{2,13})
|
|
/
|
|
)
|
|
(?: # One or more:
|
|
[^\s()<>{}\[\]]+ # Run of non-space, non-()<>{}[]
|
|
| # or
|
|
\([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
|
|
|
|
|
\([^\s]+?\) # balanced parens, non-recursive: (...)
|
|
)+
|
|
(?: # End with:
|
|
\([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
|
|
|
|
|
\([^\s]+?\) # balanced parens, non-recursive: (...)
|
|
| # or
|
|
[^\s`!()\[\]{};:'".,<>?«»“”‘’] # not a space or one of these punct chars
|
|
)
|
|
| # OR, the following to match naked domains:
|
|
(?:
|
|
(?<!@) # not preceded by a @, avoid matching foo@_gmail.com_
|
|
[a-z0-9]+
|
|
(?:[.\-][a-z0-9]+)*
|
|
[.]
|
|
(?:[a-z]{2,13})
|
|
\b
|
|
/?
|
|
(?!@) # not succeeded by a @,
|
|
# avoid matching "foo.na" in "foo.na@example.com"
|
|
)
|
|
"""
|
|
|
|
# The components of the tokenizer:
|
|
REGEXPS = (
|
|
URLS,
|
|
# Phone numbers:
|
|
r"""
|
|
(?:
|
|
(?: # (international)
|
|
\+?[01]
|
|
[ *\-.\)]*
|
|
)?
|
|
(?: # (area code)
|
|
[\(]?
|
|
\d{3}
|
|
[ *\-.\)]*
|
|
)?
|
|
\d{3} # exchange
|
|
[ *\-.\)]*
|
|
\d{4} # base
|
|
)""",
|
|
# ASCII Emoticons
|
|
EMOTICONS,
|
|
# HTML tags:
|
|
r"""<[^>\s]+>""",
|
|
# ASCII Arrows
|
|
r"""[\-]+>|<[\-]+""",
|
|
# Twitter username:
|
|
r"""(?:@[\w_]+)""",
|
|
# Twitter hashtags:
|
|
r"""(?:\#+[\w_]+[\w\'_\-]*[\w_]+)""",
|
|
# email addresses
|
|
r"""[\w.+-]+@[\w-]+\.(?:[\w-]\.?)+[\w-]""",
|
|
# Remaining word types:
|
|
r"""
|
|
(?:[^\W\d_](?:[^\W\d_]|['\-_])+[^\W\d_]) # Words with apostrophes or dashes.
|
|
|
|
|
(?:[+\-]?\d+[,/.:-]\d+[+\-]?) # Numbers, including fractions, decimals.
|
|
|
|
|
(?:[\w_]+) # Words without apostrophes or dashes.
|
|
|
|
|
(?:\.(?:\s*\.){1,}) # Ellipsis dots.
|
|
|
|
|
(?:\S) # Everything else that isn't whitespace.
|
|
""",
|
|
)
|
|
|
|
######################################################################
|
|
# This is the core tokenizing regex:
|
|
|
|
WORD_RE = regex.compile(r"""(%s)""" % "|".join(REGEXPS), regex.VERBOSE | regex.I | regex.UNICODE)
|
|
|
|
# WORD_RE performs poorly on these patterns:
|
|
HANG_RE = regex.compile(r"([^a-zA-Z0-9])\1{3,}")
|
|
|
|
# The emoticon string gets its own regex so that we can preserve case for
|
|
# them as needed:
|
|
EMOTICON_RE = regex.compile(EMOTICONS, regex.VERBOSE | regex.I | regex.UNICODE)
|
|
|
|
# These are for regularizing HTML entities to Unicode:
|
|
ENT_RE = regex.compile(r"&(#?(x?))([^&;\s]+);")
|
|
|
|
|
|
######################################################################
|
|
# Functions for converting html entities
|
|
######################################################################
|
|
|
|
|
|
def _str_to_unicode(text, encoding=None, errors="strict"):
|
|
if encoding is None:
|
|
encoding = "utf-8"
|
|
if isinstance(text, bytes):
|
|
return text.decode(encoding, errors)
|
|
return text
|
|
|
|
|
|
def _replace_html_entities(text, keep=(), remove_illegal=True, encoding="utf-8"):
|
|
"""
|
|
Remove entities from text by converting them to their
|
|
corresponding unicode character.
|
|
|
|
:param text: a unicode string or a byte string encoded in the given
|
|
`encoding` (which defaults to 'utf-8').
|
|
|
|
:param list keep: list of entity names which should not be replaced.\
|
|
This supports both numeric entities (``&#nnnn;`` and ``&#hhhh;``)
|
|
and named entities (such as `` `` or ``>``).
|
|
|
|
:param bool remove_illegal: If `True`, entities that can't be converted are\
|
|
removed. Otherwise, entities that can't be converted are kept "as
|
|
is".
|
|
|
|
:returns: A unicode string with the entities removed.
|
|
|
|
See https://github.com/scrapy/w3lib/blob/master/w3lib/html.py
|
|
|
|
>>> from nltk.tokenize.casual import _replace_html_entities
|
|
>>> _replace_html_entities(b'Price: £100')
|
|
'Price: \\xa3100'
|
|
>>> print(_replace_html_entities(b'Price: £100'))
|
|
Price: £100
|
|
>>>
|
|
"""
|
|
|
|
def _convert_entity(match):
|
|
entity_body = match.group(3)
|
|
if match.group(1):
|
|
try:
|
|
if match.group(2):
|
|
number = int(entity_body, 16)
|
|
else:
|
|
number = int(entity_body, 10)
|
|
# Numeric character references in the 80-9F range are typically
|
|
# interpreted by browsers as representing the characters mapped
|
|
# to bytes 80-9F in the Windows-1252 encoding. For more info
|
|
# see: https://en.wikipedia.org/wiki/ISO/IEC_8859-1#Similar_character_sets
|
|
if 0x80 <= number <= 0x9F:
|
|
return bytes((number,)).decode("cp1252")
|
|
except ValueError:
|
|
number = None
|
|
else:
|
|
if entity_body in keep:
|
|
return match.group(0)
|
|
else:
|
|
number = html.entities.name2codepoint.get(entity_body)
|
|
if number is not None:
|
|
try:
|
|
return chr(number)
|
|
except ValueError:
|
|
pass
|
|
|
|
return "" if remove_illegal else match.group(0)
|
|
|
|
return ENT_RE.sub(_convert_entity, _str_to_unicode(text, encoding))
|
|
|
|
|
|
######################################################################
|
|
|
|
|
|
class TweetTokenizer:
|
|
r"""
|
|
Tokenizer for tweets.
|
|
|
|
>>> from nltk.tokenize import TweetTokenizer
|
|
>>> tknzr = TweetTokenizer()
|
|
>>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
|
|
>>> tknzr.tokenize(s0)
|
|
['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--']
|
|
|
|
Examples using `strip_handles` and `reduce_len parameters`:
|
|
|
|
>>> tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)
|
|
>>> s1 = '@remy: This is waaaaayyyy too much for you!!!!!!'
|
|
>>> tknzr.tokenize(s1)
|
|
[':', 'This', 'is', 'waaayyy', 'too', 'much', 'for', 'you', '!', '!', '!']
|
|
"""
|
|
|
|
def __init__(self, preserve_case=True, reduce_len=False, strip_handles=False):
|
|
self.preserve_case = preserve_case
|
|
self.reduce_len = reduce_len
|
|
self.strip_handles = strip_handles
|
|
|
|
def tokenize(self, text):
|
|
"""
|
|
:param text: str
|
|
:rtype: list(str)
|
|
:return: a tokenized list of strings; concatenating this list returns\
|
|
the original string if `preserve_case=False`
|
|
"""
|
|
# Fix HTML character entities:
|
|
text = _replace_html_entities(text)
|
|
# Remove username handles
|
|
if self.strip_handles:
|
|
text = remove_handles(text)
|
|
# Normalize word lengthening
|
|
if self.reduce_len:
|
|
text = reduce_lengthening(text)
|
|
# Shorten problematic sequences of characters
|
|
safe_text = HANG_RE.sub(r"\1\1\1", text)
|
|
# Tokenize:
|
|
words = WORD_RE.findall(safe_text)
|
|
# Possibly alter the case, but avoid changing emoticons like :D into :d:
|
|
if not self.preserve_case:
|
|
words = list(
|
|
map((lambda x: x if EMOTICON_RE.search(x) else x.lower()), words)
|
|
)
|
|
return words
|
|
|
|
|
|
######################################################################
|
|
# Normalization Functions
|
|
######################################################################
|
|
|
|
|
|
def reduce_lengthening(text):
|
|
"""
|
|
Replace repeated character sequences of length 3 or greater with sequences
|
|
of length 3.
|
|
"""
|
|
pattern = regex.compile(r"(.)\1{2,}")
|
|
return pattern.sub(r"\1\1\1", text)
|
|
|
|
|
|
def remove_handles(text):
|
|
"""
|
|
Remove Twitter username handles from text.
|
|
"""
|
|
pattern = regex.compile(
|
|
r"(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){20}(?!@))|(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){1,19})(?![A-Za-z0-9_]*@)"
|
|
)
|
|
# Substitute handles with ' ' to ensure that text on either side of removed handles are tokenized correctly
|
|
return pattern.sub(" ", text)
|
|
|
|
|
|
######################################################################
|
|
# Tokenization Function
|
|
######################################################################
|
|
|
|
|
|
def casual_tokenize(text, preserve_case=True, reduce_len=False, strip_handles=False):
|
|
"""
|
|
Convenience function for wrapping the tokenizer.
|
|
"""
|
|
return TweetTokenizer(
|
|
preserve_case=preserve_case, reduce_len=reduce_len, strip_handles=strip_handles
|
|
).tokenize(text)
|
|
|
|
|
|
###############################################################################
|