# Natural Language Toolkit: Pros and Cons Corpus Reader
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Pierpaolo Pantone <24alsecondo@gmail.com>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
"""
CorpusReader for the Pros and Cons dataset.
- Pros and Cons dataset information -
Contact: Bing Liu, liub@cs.uic.edu
https://www.cs.uic.edu/~liub
Distributed with permission.
Related papers:
- Murthy Ganapathibhotla and Bing Liu. "Mining Opinions in Comparative Sentences".
Proceedings of the 22nd International Conference on Computational Linguistics
(Coling-2008), Manchester, 18-22 August, 2008.
- Bing Liu, Minqing Hu and Junsheng Cheng. "Opinion Observer: Analyzing and Comparing
Opinions on the Web". Proceedings of the 14th international World Wide Web
conference (WWW-2005), May 10-14, 2005, in Chiba, Japan.
"""
import re
from nltk.corpus.reader.api import *
from nltk.tokenize import *
[docs]class ProsConsCorpusReader(CategorizedCorpusReader, CorpusReader):
"""
Reader for the Pros and Cons sentence dataset.
>>> from nltk.corpus import pros_cons
>>> pros_cons.sents(categories='Cons') # doctest: +NORMALIZE_WHITESPACE
[['East', 'batteries', '!', 'On', '-', 'off', 'switch', 'too', 'easy',
'to', 'maneuver', '.'], ['Eats', '...', 'no', ',', 'GULPS', 'batteries'],
...]
>>> pros_cons.words('IntegratedPros.txt')
['Easy', 'to', 'use', ',', 'economical', '!', ...]
"""
CorpusView = StreamBackedCorpusView
[docs] def __init__(
self,
root,
fileids,
word_tokenizer=WordPunctTokenizer(),
encoding="utf8",
**kwargs
):
"""
:param root: The root directory for the corpus.
:param fileids: a list or regexp specifying the fileids in the corpus.
:param word_tokenizer: a tokenizer for breaking sentences or paragraphs
into words. Default: `WhitespaceTokenizer`
:param encoding: the encoding that should be used to read the corpus.
:param kwargs: additional parameters passed to CategorizedCorpusReader.
"""
CorpusReader.__init__(self, root, fileids, encoding)
CategorizedCorpusReader.__init__(self, kwargs)
self._word_tokenizer = word_tokenizer
[docs] def sents(self, fileids=None, categories=None):
"""
Return all sentences in the corpus or in the specified files/categories.
:param fileids: a list or regexp specifying the ids of the files whose
sentences have to be returned.
:param categories: a list specifying the categories whose sentences
have to be returned.
:return: the given file(s) as a list of sentences. Each sentence is
tokenized using the specified word_tokenizer.
:rtype: list(list(str))
"""
fileids = self._resolve(fileids, categories)
if fileids is None:
fileids = self._fileids
elif isinstance(fileids, str):
fileids = [fileids]
return concat(
[
self.CorpusView(path, self._read_sent_block, encoding=enc)
for (path, enc, fileid) in self.abspaths(fileids, True, True)
]
)
[docs] def words(self, fileids=None, categories=None):
"""
Return all words and punctuation symbols in the corpus or in the specified
files/categories.
:param fileids: a list or regexp specifying the ids of the files whose
words have to be returned.
:param categories: a list specifying the categories whose words have
to be returned.
:return: the given file(s) as a list of words and punctuation symbols.
:rtype: list(str)
"""
fileids = self._resolve(fileids, categories)
if fileids is None:
fileids = self._fileids
elif isinstance(fileids, str):
fileids = [fileids]
return concat(
[
self.CorpusView(path, self._read_word_block, encoding=enc)
for (path, enc, fileid) in self.abspaths(fileids, True, True)
]
)
def _read_sent_block(self, stream):
sents = []
for i in range(20): # Read 20 lines at a time.
line = stream.readline()
if not line:
continue
sent = re.match(r"^(?!\n)\s*<(Pros|Cons)>(.*)</(?:Pros|Cons)>", line)
if sent:
sents.append(self._word_tokenizer.tokenize(sent.group(2).strip()))
return sents
def _read_word_block(self, stream):
words = []
for sent in self._read_sent_block(stream):
words.extend(sent)
return words