# Natural Language Toolkit: SentiWordNet
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Christopher Potts <cgpotts@stanford.edu>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
"""
An NLTK interface for SentiWordNet
SentiWordNet is a lexical resource for opinion mining.
SentiWordNet assigns to each synset of WordNet three
sentiment scores: positivity, negativity, and objectivity.
For details about SentiWordNet see:
http://sentiwordnet.isti.cnr.it/
>>> from nltk.corpus import sentiwordnet as swn
>>> print(swn.senti_synset('breakdown.n.03'))
<breakdown.n.03: PosScore=0.0 NegScore=0.25>
>>> list(swn.senti_synsets('slow'))
[SentiSynset('decelerate.v.01'), SentiSynset('slow.v.02'),\
SentiSynset('slow.v.03'), SentiSynset('slow.a.01'),\
SentiSynset('slow.a.02'), SentiSynset('dense.s.04'),\
SentiSynset('slow.a.04'), SentiSynset('boring.s.01'),\
SentiSynset('dull.s.08'), SentiSynset('slowly.r.01'),\
SentiSynset('behind.r.03')]
>>> happy = swn.senti_synsets('happy', 'a')
>>> happy0 = list(happy)[0]
>>> happy0.pos_score()
0.875
>>> happy0.neg_score()
0.0
>>> happy0.obj_score()
0.125
"""
import re
from nltk.corpus.reader import CorpusReader
[docs]class SentiWordNetCorpusReader(CorpusReader):
[docs] def __init__(self, root, fileids, encoding="utf-8"):
"""
Construct a new SentiWordNet Corpus Reader, using data from
the specified file.
"""
super().__init__(root, fileids, encoding=encoding)
if len(self._fileids) != 1:
raise ValueError("Exactly one file must be specified")
self._db = {}
self._parse_src_file()
def _parse_src_file(self):
lines = self.open(self._fileids[0]).read().splitlines()
lines = filter((lambda x: not re.search(r"^\s*#", x)), lines)
for i, line in enumerate(lines):
fields = [field.strip() for field in re.split(r"\t+", line)]
try:
pos, offset, pos_score, neg_score, synset_terms, gloss = fields
except BaseException as e:
raise ValueError(f"Line {i} formatted incorrectly: {line}\n") from e
if pos and offset:
offset = int(offset)
self._db[(pos, offset)] = (float(pos_score), float(neg_score))
[docs] def senti_synset(self, *vals):
from nltk.corpus import wordnet as wn
if tuple(vals) in self._db:
pos_score, neg_score = self._db[tuple(vals)]
pos, offset = vals
if pos == "s":
pos = "a"
synset = wn.synset_from_pos_and_offset(pos, offset)
return SentiSynset(pos_score, neg_score, synset)
else:
synset = wn.synset(vals[0])
pos = synset.pos()
if pos == "s":
pos = "a"
offset = synset.offset()
if (pos, offset) in self._db:
pos_score, neg_score = self._db[(pos, offset)]
return SentiSynset(pos_score, neg_score, synset)
else:
return None
[docs] def senti_synsets(self, string, pos=None):
from nltk.corpus import wordnet as wn
sentis = []
synset_list = wn.synsets(string, pos)
for synset in synset_list:
sentis.append(self.senti_synset(synset.name()))
sentis = filter(lambda x: x, sentis)
return sentis
[docs] def all_senti_synsets(self):
from nltk.corpus import wordnet as wn
for key, fields in self._db.items():
pos, offset = key
pos_score, neg_score = fields
synset = wn.synset_from_pos_and_offset(pos, offset)
yield SentiSynset(pos_score, neg_score, synset)
[docs]class SentiSynset:
[docs] def __init__(self, pos_score, neg_score, synset):
self._pos_score = pos_score
self._neg_score = neg_score
self._obj_score = 1.0 - (self._pos_score + self._neg_score)
self.synset = synset
[docs] def pos_score(self):
return self._pos_score
[docs] def neg_score(self):
return self._neg_score
[docs] def obj_score(self):
return self._obj_score
def __str__(self):
"""Prints just the Pos/Neg scores for now."""
s = "<"
s += self.synset.name() + ": "
s += "PosScore=%s " % self._pos_score
s += "NegScore=%s" % self._neg_score
s += ">"
return s
def __repr__(self):
return "Senti" + repr(self.synset)