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from nltk.classify import NaiveBayesClassifier
from nltk.corpus import subjectivity
from nltk.sentiment import SentimentAnalyzer
from nltk.sentiment.util import *
from nltk.sentiment.vader import SentimentIntensityAnalyzer
class SharpClassifier(object):
def __init__(self, sentence):
self.sentence = sentence
print("Classification....")
def classify(self):
sentence = self.sentence
n_instances = 100
subj_docs = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')[:n_instances]]
obj_docs = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:n_instances]]
len(subj_docs), len(obj_docs)
train_subj_docs = subj_docs[:80]
test_subj_docs = subj_docs[80:100]
train_obj_docs = obj_docs[:80]
test_obj_docs = obj_docs[80:100]
training_docs = train_subj_docs + train_obj_docs
testing_docs = test_subj_docs + test_obj_docs
sentim_analyzer = SentimentAnalyzer()
all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in training_docs])
unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
training_set = sentim_analyzer.apply_features(training_docs)
test_set = sentim_analyzer.apply_features(testing_docs)
trainer = NaiveBayesClassifier.train
classifier = sentim_analyzer.train(trainer, training_set)
# for key,value in sorted(sentim_analyzer.evaluate(test_set).items()):
# print('{0}: {1}'.format(key, value))
sid = SentimentIntensityAnalyzer()
ss = sid.polarity_scores(sentence)
polarity = ''
if ss['neg'] < ss['pos']:
polarity = 'partialSolution'
elif ss['neg'] > ss['pos']:
polarity = 'problem'
else:
polarity ='neutre'
# for k in sorted(ss):
# print('{0}: {1}, '.format(k, ss[k]), end='')
return polarity
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