138 lines
4.4 KiB
Python
138 lines
4.4 KiB
Python
# -*- coding: utf-8 -*-
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# Natural Language Toolkit: SentiWordNet
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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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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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An NLTK interface for SentiWordNet
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SentiWordNet is a lexical resource for opinion mining.
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SentiWordNet assigns to each synset of WordNet three
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sentiment scores: positivity, negativity, and objectivity.
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For details about SentiWordNet see:
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http://sentiwordnet.isti.cnr.it/
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>>> from nltk.corpus import sentiwordnet as swn
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>>> print(swn.senti_synset('breakdown.n.03'))
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<breakdown.n.03: PosScore=0.0 NegScore=0.25>
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>>> list(swn.senti_synsets('slow'))
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[SentiSynset('decelerate.v.01'), SentiSynset('slow.v.02'),
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SentiSynset('slow.v.03'), SentiSynset('slow.a.01'),
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SentiSynset('slow.a.02'), SentiSynset('dense.s.04'),
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SentiSynset('slow.a.04'), SentiSynset('boring.s.01'),
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SentiSynset('dull.s.08'), SentiSynset('slowly.r.01'),
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SentiSynset('behind.r.03')]
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>>> happy = swn.senti_synsets('happy', 'a')
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>>> happy0 = list(happy)[0]
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>>> happy0.pos_score()
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0.875
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>>> happy0.neg_score()
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0.0
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>>> happy0.obj_score()
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0.125
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"""
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import re
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from nltk.corpus.reader import CorpusReader
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class SentiWordNetCorpusReader(CorpusReader):
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def __init__(self, root, fileids, encoding="utf-8"):
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"""
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Construct a new SentiWordNet Corpus Reader, using data from
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the specified file.
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"""
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super(SentiWordNetCorpusReader, self).__init__(root, fileids, encoding=encoding)
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if len(self._fileids) != 1:
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raise ValueError("Exactly one file must be specified")
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self._db = {}
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self._parse_src_file()
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def _parse_src_file(self):
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lines = self.open(self._fileids[0]).read().splitlines()
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lines = filter((lambda x: not re.search(r"^\s*#", x)), lines)
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for i, line in enumerate(lines):
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fields = [field.strip() for field in re.split(r"\t+", line)]
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try:
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pos, offset, pos_score, neg_score, synset_terms, gloss = fields
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except:
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raise ValueError("Line %s formatted incorrectly: %s\n" % (i, line))
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if pos and offset:
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offset = int(offset)
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self._db[(pos, offset)] = (float(pos_score), float(neg_score))
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def senti_synset(self, *vals):
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from nltk.corpus import wordnet as wn
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if tuple(vals) in self._db:
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pos_score, neg_score = self._db[tuple(vals)]
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pos, offset = vals
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if pos == "s":
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pos = "a"
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synset = wn.synset_from_pos_and_offset(pos, offset)
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return SentiSynset(pos_score, neg_score, synset)
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else:
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synset = wn.synset(vals[0])
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pos = synset.pos()
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if pos == "s":
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pos = "a"
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offset = synset.offset()
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if (pos, offset) in self._db:
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pos_score, neg_score = self._db[(pos, offset)]
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return SentiSynset(pos_score, neg_score, synset)
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else:
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return None
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def senti_synsets(self, string, pos=None):
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from nltk.corpus import wordnet as wn
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sentis = []
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synset_list = wn.synsets(string, pos)
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for synset in synset_list:
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sentis.append(self.senti_synset(synset.name()))
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sentis = filter(lambda x: x, sentis)
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return sentis
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def all_senti_synsets(self):
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from nltk.corpus import wordnet as wn
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for key, fields in self._db.items():
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pos, offset = key
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pos_score, neg_score = fields
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synset = wn.synset_from_pos_and_offset(pos, offset)
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yield SentiSynset(pos_score, neg_score, synset)
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class SentiSynset(object):
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def __init__(self, pos_score, neg_score, synset):
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self._pos_score = pos_score
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self._neg_score = neg_score
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self._obj_score = 1.0 - (self._pos_score + self._neg_score)
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self.synset = synset
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def pos_score(self):
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return self._pos_score
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def neg_score(self):
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return self._neg_score
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def obj_score(self):
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return self._obj_score
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def __str__(self):
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"""Prints just the Pos/Neg scores for now."""
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s = "<"
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s += self.synset.name() + ": "
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s += "PosScore=%s " % self._pos_score
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s += "NegScore=%s" % self._neg_score
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s += ">"
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return s
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def __repr__(self):
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return "Senti" + repr(self.synset)
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