144 lines
5.1 KiB
Python
144 lines
5.1 KiB
Python
# Natural Language Toolkit: Pros and Cons Corpus Reader
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Pierpaolo Pantone <24alsecondo@gmail.com>
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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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CorpusReader for the Pros and Cons dataset.
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- Pros and Cons dataset information -
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Contact: Bing Liu, liub@cs.uic.edu
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http://www.cs.uic.edu/~liub
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Distributed with permission.
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Related papers:
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- Murthy Ganapathibhotla and Bing Liu. "Mining Opinions in Comparative Sentences".
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Proceedings of the 22nd International Conference on Computational Linguistics
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(Coling-2008), Manchester, 18-22 August, 2008.
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- Bing Liu, Minqing Hu and Junsheng Cheng. "Opinion Observer: Analyzing and Comparing
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Opinions on the Web". Proceedings of the 14th international World Wide Web
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conference (WWW-2005), May 10-14, 2005, in Chiba, Japan.
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"""
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import re
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from six import string_types
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from nltk.corpus.reader.api import *
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from nltk.tokenize import *
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class ProsConsCorpusReader(CategorizedCorpusReader, CorpusReader):
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"""
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Reader for the Pros and Cons sentence dataset.
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>>> from nltk.corpus import pros_cons
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>>> pros_cons.sents(categories='Cons')
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[['East', 'batteries', '!', 'On', '-', 'off', 'switch', 'too', 'easy',
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'to', 'maneuver', '.'], ['Eats', '...', 'no', ',', 'GULPS', 'batteries'],
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...]
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>>> pros_cons.words('IntegratedPros.txt')
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['Easy', 'to', 'use', ',', 'economical', '!', ...]
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"""
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CorpusView = StreamBackedCorpusView
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def __init__(
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self,
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root,
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fileids,
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word_tokenizer=WordPunctTokenizer(),
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encoding="utf8",
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**kwargs
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):
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"""
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:param root: The root directory for the corpus.
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:param fileids: a list or regexp specifying the fileids in the corpus.
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:param word_tokenizer: a tokenizer for breaking sentences or paragraphs
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into words. Default: `WhitespaceTokenizer`
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:param encoding: the encoding that should be used to read the corpus.
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:param kwargs: additional parameters passed to CategorizedCorpusReader.
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"""
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CorpusReader.__init__(self, root, fileids, encoding)
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CategorizedCorpusReader.__init__(self, kwargs)
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self._word_tokenizer = word_tokenizer
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def sents(self, fileids=None, categories=None):
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"""
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Return all sentences in the corpus or in the specified files/categories.
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:param fileids: a list or regexp specifying the ids of the files whose
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sentences have to be returned.
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:param categories: a list specifying the categories whose sentences
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have to be returned.
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:return: the given file(s) as a list of sentences. Each sentence is
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tokenized using the specified word_tokenizer.
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:rtype: list(list(str))
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"""
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fileids = self._resolve(fileids, categories)
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if fileids is None:
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fileids = self._fileids
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elif isinstance(fileids, string_types):
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fileids = [fileids]
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return concat(
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[
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self.CorpusView(path, self._read_sent_block, encoding=enc)
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for (path, enc, fileid) in self.abspaths(fileids, True, True)
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]
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)
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def words(self, fileids=None, categories=None):
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"""
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Return all words and punctuation symbols in the corpus or in the specified
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files/categories.
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:param fileids: a list or regexp specifying the ids of the files whose
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words have to be returned.
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:param categories: a list specifying the categories whose words have
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to be returned.
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:return: the given file(s) as a list of words and punctuation symbols.
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:rtype: list(str)
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"""
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fileids = self._resolve(fileids, categories)
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if fileids is None:
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fileids = self._fileids
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elif isinstance(fileids, string_types):
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fileids = [fileids]
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return concat(
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[
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self.CorpusView(path, self._read_word_block, encoding=enc)
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for (path, enc, fileid) in self.abspaths(fileids, True, True)
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]
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)
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def _read_sent_block(self, stream):
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sents = []
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for i in range(20): # Read 20 lines at a time.
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line = stream.readline()
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if not line:
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continue
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sent = re.match(r"^(?!\n)\s*<(Pros|Cons)>(.*)</(?:Pros|Cons)>", line)
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if sent:
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sents.append(self._word_tokenizer.tokenize(sent.group(2).strip()))
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return sents
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def _read_word_block(self, stream):
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words = []
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for sent in self._read_sent_block(stream):
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words.extend(sent)
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return words
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def _resolve(self, fileids, categories):
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if fileids is not None and categories is not None:
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raise ValueError("Specify fileids or categories, not both")
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if categories is not None:
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return self.fileids(categories)
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else:
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return fileids
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