348 lines
11 KiB
Python
348 lines
11 KiB
Python
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# coding: utf-8
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#
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# Natural Language Toolkit: Twitter Tokenizer
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Christopher Potts <cgpotts@stanford.edu>
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# Ewan Klein <ewan@inf.ed.ac.uk> (modifications)
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# Pierpaolo Pantone <> (modifications)
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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#
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"""
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Twitter-aware tokenizer, designed to be flexible and easy to adapt to new
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domains and tasks. The basic logic is this:
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1. The tuple regex_strings defines a list of regular expression
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strings.
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2. The regex_strings strings are put, in order, into a compiled
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regular expression object called word_re.
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3. The tokenization is done by word_re.findall(s), where s is the
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user-supplied string, inside the tokenize() method of the class
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Tokenizer.
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4. When instantiating Tokenizer objects, there is a single option:
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preserve_case. By default, it is set to True. If it is set to
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False, then the tokenizer will downcase everything except for
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emoticons.
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"""
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######################################################################
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from __future__ import unicode_literals
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import re
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from six import int2byte, unichr
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from six.moves import html_entities
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######################################################################
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# The following strings are components in the regular expression
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# that is used for tokenizing. It's important that phone_number
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# appears first in the final regex (since it can contain whitespace).
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# It also could matter that tags comes after emoticons, due to the
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# possibility of having text like
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#
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# <:| and some text >:)
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#
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# Most importantly, the final element should always be last, since it
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# does a last ditch whitespace-based tokenization of whatever is left.
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# ToDo: Update with http://en.wikipedia.org/wiki/List_of_emoticons ?
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# This particular element is used in a couple ways, so we define it
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# with a name:
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EMOTICONS = r"""
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(?:
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[<>]?
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[:;=8] # eyes
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[\-o\*\']? # optional nose
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[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
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[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
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[\-o\*\']? # optional nose
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[:;=8] # eyes
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[<>]?
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<3 # heart
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)"""
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# URL pattern due to John Gruber, modified by Tom Winzig. See
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# https://gist.github.com/winzig/8894715
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URLS = r""" # Capture 1: entire matched URL
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(?:
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https?: # URL protocol and colon
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(?:
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/{1,3} # 1-3 slashes
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| # or
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[a-z0-9%] # Single letter or digit or '%'
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# (Trying not to match e.g. "URI::Escape")
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)
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| # or
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# looks like domain name followed by a slash:
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[a-z0-9.\-]+[.]
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(?:[a-z]{2,13})
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/
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)
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(?: # One or more:
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[^\s()<>{}\[\]]+ # Run of non-space, non-()<>{}[]
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| # or
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\([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
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\([^\s]+?\) # balanced parens, non-recursive: (...)
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)+
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(?: # End with:
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\([^\s()]*?\([^\s()]+\)[^\s()]*?\) # balanced parens, one level deep: (...(...)...)
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\([^\s]+?\) # balanced parens, non-recursive: (...)
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| # or
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[^\s`!()\[\]{};:'".,<>?«»“”‘’] # not a space or one of these punct chars
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)
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| # OR, the following to match naked domains:
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(?:
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(?<!@) # not preceded by a @, avoid matching foo@_gmail.com_
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[a-z0-9]+
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(?:[.\-][a-z0-9]+)*
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[.]
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(?:[a-z]{2,13})
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\b
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/?
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(?!@) # not succeeded by a @,
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# avoid matching "foo.na" in "foo.na@example.com"
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)
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"""
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# The components of the tokenizer:
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REGEXPS = (
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URLS,
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# Phone numbers:
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r"""
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(?:
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(?: # (international)
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\+?[01]
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[ *\-.\)]*
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)?
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(?: # (area code)
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[\(]?
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\d{3}
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[ *\-.\)]*
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)?
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\d{3} # exchange
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[ *\-.\)]*
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\d{4} # base
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)""",
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# ASCII Emoticons
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EMOTICONS,
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# HTML tags:
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r"""<[^>\s]+>""",
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# ASCII Arrows
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r"""[\-]+>|<[\-]+""",
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# Twitter username:
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r"""(?:@[\w_]+)""",
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# Twitter hashtags:
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r"""(?:\#+[\w_]+[\w\'_\-]*[\w_]+)""",
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# email addresses
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r"""[\w.+-]+@[\w-]+\.(?:[\w-]\.?)+[\w-]""",
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# Remaining word types:
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r"""
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(?:[^\W\d_](?:[^\W\d_]|['\-_])+[^\W\d_]) # Words with apostrophes or dashes.
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(?:[+\-]?\d+[,/.:-]\d+[+\-]?) # Numbers, including fractions, decimals.
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(?:[\w_]+) # Words without apostrophes or dashes.
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(?:\.(?:\s*\.){1,}) # Ellipsis dots.
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(?:\S) # Everything else that isn't whitespace.
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""",
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)
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######################################################################
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# This is the core tokenizing regex:
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WORD_RE = re.compile(r"""(%s)""" % "|".join(REGEXPS), re.VERBOSE | re.I | re.UNICODE)
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# WORD_RE performs poorly on these patterns:
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HANG_RE = re.compile(r'([^a-zA-Z0-9])\1{3,}')
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# The emoticon string gets its own regex so that we can preserve case for
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# them as needed:
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EMOTICON_RE = re.compile(EMOTICONS, re.VERBOSE | re.I | re.UNICODE)
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# These are for regularizing HTML entities to Unicode:
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ENT_RE = re.compile(r'&(#?(x?))([^&;\s]+);')
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######################################################################
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# Functions for converting html entities
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######################################################################
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def _str_to_unicode(text, encoding=None, errors='strict'):
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if encoding is None:
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encoding = 'utf-8'
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if isinstance(text, bytes):
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return text.decode(encoding, errors)
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return text
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def _replace_html_entities(text, keep=(), remove_illegal=True, encoding='utf-8'):
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"""
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Remove entities from text by converting them to their
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corresponding unicode character.
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:param text: a unicode string or a byte string encoded in the given
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`encoding` (which defaults to 'utf-8').
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:param list keep: list of entity names which should not be replaced.\
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This supports both numeric entities (``&#nnnn;`` and ``&#hhhh;``)
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and named entities (such as `` `` or ``>``).
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:param bool remove_illegal: If `True`, entities that can't be converted are\
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removed. Otherwise, entities that can't be converted are kept "as
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is".
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:returns: A unicode string with the entities removed.
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See https://github.com/scrapy/w3lib/blob/master/w3lib/html.py
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>>> from nltk.tokenize.casual import _replace_html_entities
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>>> _replace_html_entities(b'Price: £100')
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'Price: \\xa3100'
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>>> print(_replace_html_entities(b'Price: £100'))
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Price: £100
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>>>
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"""
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def _convert_entity(match):
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entity_body = match.group(3)
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if match.group(1):
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try:
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if match.group(2):
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number = int(entity_body, 16)
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else:
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number = int(entity_body, 10)
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# Numeric character references in the 80-9F range are typically
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# interpreted by browsers as representing the characters mapped
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# to bytes 80-9F in the Windows-1252 encoding. For more info
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# see: https://en.wikipedia.org/wiki/ISO/IEC_8859-1#Similar_character_sets
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if 0x80 <= number <= 0x9F:
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return int2byte(number).decode('cp1252')
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except ValueError:
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number = None
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else:
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if entity_body in keep:
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return match.group(0)
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else:
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number = html_entities.name2codepoint.get(entity_body)
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if number is not None:
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try:
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return unichr(number)
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except ValueError:
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pass
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return "" if remove_illegal else match.group(0)
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return ENT_RE.sub(_convert_entity, _str_to_unicode(text, encoding))
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######################################################################
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class TweetTokenizer:
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r"""
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Tokenizer for tweets.
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>>> from nltk.tokenize import TweetTokenizer
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>>> tknzr = TweetTokenizer()
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>>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
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>>> tknzr.tokenize(s0)
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['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--']
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Examples using `strip_handles` and `reduce_len parameters`:
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>>> tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)
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>>> s1 = '@remy: This is waaaaayyyy too much for you!!!!!!'
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>>> tknzr.tokenize(s1)
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[':', 'This', 'is', 'waaayyy', 'too', 'much', 'for', 'you', '!', '!', '!']
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"""
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def __init__(self, preserve_case=True, reduce_len=False, strip_handles=False):
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self.preserve_case = preserve_case
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self.reduce_len = reduce_len
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self.strip_handles = strip_handles
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def tokenize(self, text):
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"""
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:param text: str
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:rtype: list(str)
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:return: a tokenized list of strings; concatenating this list returns\
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the original string if `preserve_case=False`
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"""
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# Fix HTML character entities:
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text = _replace_html_entities(text)
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# Remove username handles
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if self.strip_handles:
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text = remove_handles(text)
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# Normalize word lengthening
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if self.reduce_len:
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text = reduce_lengthening(text)
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# Shorten problematic sequences of characters
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safe_text = HANG_RE.sub(r'\1\1\1', text)
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# Tokenize:
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words = WORD_RE.findall(safe_text)
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# Possibly alter the case, but avoid changing emoticons like :D into :d:
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if not self.preserve_case:
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words = list(
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map((lambda x: x if EMOTICON_RE.search(x) else x.lower()), words)
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)
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return words
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######################################################################
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# Normalization Functions
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######################################################################
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def reduce_lengthening(text):
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"""
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Replace repeated character sequences of length 3 or greater with sequences
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of length 3.
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"""
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pattern = re.compile(r"(.)\1{2,}")
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return pattern.sub(r"\1\1\1", text)
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def remove_handles(text):
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"""
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Remove Twitter username handles from text.
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"""
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pattern = re.compile(
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r"(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){20}(?!@))|(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){1,19})(?![A-Za-z0-9_]*@)"
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)
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# Substitute handles with ' ' to ensure that text on either side of removed handles are tokenized correctly
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return pattern.sub(' ', text)
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######################################################################
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# Tokenization Function
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######################################################################
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def casual_tokenize(text, preserve_case=True, reduce_len=False, strip_handles=False):
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"""
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Convenience function for wrapping the tokenizer.
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"""
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return TweetTokenizer(
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preserve_case=preserve_case, reduce_len=reduce_len, strip_handles=strip_handles
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).tokenize(text)
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###############################################################################
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