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import math
import nltk
from nltk.corpus import wordnet as wn
import nltk.classify.util
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews
from sets import Set
class Solver:
def demo(self):
def word_feats(words):
return dict([(word, True) for word in words])
def expand_sets(positive,negative,neutral):
newPositive = set(positive)
newNegative = set(negative)
newNeutral = set(neutral)
# Add Syns to Positive
for word in positive:
for syn in wn.synsets(word, pos=wn.ADJ):
for lemma in syn.lemmas():
curr = lemma.name().split('.')[0]
if(curr not in newNegative and curr not in newNeutral):
newPositive.add(curr)
elif( curr in newNegative):
newNegative.discard(curr)
newNeutral.add(curr)
# Deal with antonyms
for ant in lemma.antonyms():
if(ant not in newPositive and ant not in newNeutral):
newNegative.add(ant)
elif(ant in newPositive):
newPositive.discard(ant)
newNeutral.add(ant)
# Add Syns to Negative
for word in negative:
for syn in wn.synsets(word, pos=wn.ADJ):
for lemma in syn.lemmas():
curr = lemma.name().split('.')[0]
print curr
if(curr not in newPositive and curr not in newNeutral):
newNegative.add(curr)
elif(curr in newPositive):
newPositive.discard(curr)
newNeutral.add(curr)
# Deal with antonyms
for ant in lemma.antonyms():
if(ant not in newNegative and ant not in newNeutral):
newPositive.add(ant)
elif(ant in newNegative):
newNegative.discard(ant)
newNeutral.add(ant)
return (newPositive,newNegative,newNeutral)
# Set up initial Sets S_p and S_n
positive = Set(['Good'])
negative = Set(['Bad'])
neutral = Set([''])
# Expand on Sets to get S_p' and S_n'
for num in range(1,2):
newsets = expand_sets(positive,negative,neutral);
positive = set(newsets[0])
negative = set(newsets[1])
neutral = set(newsets[2])
print positive
print negative
# # Learn Classifier
# trainfeats = [({word : True},"pos") for word in positive] + [({word : True},"neg") for word in negative]
#
# #negfeats = [({'insulting': True},'neg'),({'bad':True},'neg')]
#
# #trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
# classifier = NaiveBayesClassifier.train(trainfeats)
#
#
# # Testing
# negids = movie_reviews.fileids('neg')
# posids = movie_reviews.fileids('pos')
#
# negfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'neg') for f in negids]
# posfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'pos') for f in posids]
# negcutoff = len(negfeats)*3/4
# poscutoff = len(posfeats)*3/4
#
# testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]
#
# print 'Dictionary of %d positive words and %d negative words, tested on %d instances' % (len(positive),len(negative), len(testfeats))
#
# print 'accuracy:', nltk.classify.util.accuracy(classifier, testfeats)
# classifier.show_most_informative_features()
#text = nltk.word_tokenize("And now for a production unlike any other a very fuzzy and cute dog")
#print(text)
#text = nltk.pos_tag(text)
#print(text)
#for token in text:
# if(token[1] == "JJ" or token[1] == "JJR" or token[1] == "JJS"):
# print(wn.synsets(token[0]))
Solver().demo()
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