Source code for nltk.corpus.reader.opinion_lexicon

# Natural Language Toolkit: Opinion Lexicon Corpus Reader
#
# Copyright (C) 2001-2017 NLTK Project
# Author: Pierpaolo Pantone <24alsecondo@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
CorpusReader for the Opinion Lexicon.

- Opinion Lexicon information -
Authors: Minqing Hu and Bing Liu, 2004.
    Department of Computer Sicence
    University of Illinois at Chicago

Contact: Bing Liu, liub@cs.uic.edu
        http://www.cs.uic.edu/~liub

Distributed with permission.

Related papers:
- Minqing Hu and Bing Liu. "Mining and summarizing customer reviews".
    Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery
    & Data Mining (KDD-04), Aug 22-25, 2004, Seattle, Washington, USA.

- 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, Chiba, Japan.
"""

from nltk.compat import string_types
from nltk.corpus.reader import WordListCorpusReader
from nltk.corpus.reader.api import *

[docs]class IgnoreReadmeCorpusView(StreamBackedCorpusView): """ This CorpusView is used to skip the initial readme block of the corpus. """ def __init__(self, *args, **kwargs): StreamBackedCorpusView.__init__(self, *args, **kwargs) # open self._stream self._open() # skip the readme block read_blankline_block(self._stream) # Set the initial position to the current stream position self._filepos = [self._stream.tell()]
[docs]class OpinionLexiconCorpusReader(WordListCorpusReader): """ Reader for Liu and Hu opinion lexicon. Blank lines and readme are ignored. >>> from nltk.corpus import opinion_lexicon >>> opinion_lexicon.words() ['2-faced', '2-faces', 'abnormal', 'abolish', ...] The OpinionLexiconCorpusReader provides shortcuts to retrieve positive/negative words: >>> opinion_lexicon.negative() ['2-faced', '2-faces', 'abnormal', 'abolish', ...] Note that words from `words()` method are sorted by file id, not alphabetically: >>> opinion_lexicon.words()[0:10] ['2-faced', '2-faces', 'abnormal', 'abolish', 'abominable', 'abominably', 'abominate', 'abomination', 'abort', 'aborted'] >>> sorted(opinion_lexicon.words())[0:10] ['2-faced', '2-faces', 'a+', 'abnormal', 'abolish', 'abominable', 'abominably', 'abominate', 'abomination', 'abort'] """ CorpusView = IgnoreReadmeCorpusView
[docs] def words(self, fileids=None): """ Return all words in the opinion lexicon. Note that these words are not sorted in alphabetical order. :param fileids: a list or regexp specifying the ids of the files whose words have to be returned. :return: the given file(s) as a list of words and punctuation symbols. :rtype: list(str) """ if fileids is None: fileids = self._fileids elif isinstance(fileids, compat.string_types): fileids = [fileids] return concat([self.CorpusView(path, self._read_word_block, encoding=enc) for (path, enc, fileid) in self.abspaths(fileids, True, True)])
[docs] def positive(self): """ Return all positive words in alphabetical order. :return: a list of positive words. :rtype: list(str) """ return self.words('positive-words.txt')
[docs] def negative(self): """ Return all negative words in alphabetical order. :return: a list of negative words. :rtype: list(str) """ return self.words('negative-words.txt')
def _read_word_block(self, stream): words = [] for i in range(20): # Read 20 lines at a time. line = stream.readline() if not line: continue words.append(line.strip()) return words