class SentimentClassifier():
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.cl_helper = ClassifierHelper()
self.wsd_helper = WSDHelper()
self.ab_path = os.path.dirname(os.path.abspath(__file__))
def subjective_and_objective_classification(self ,sentence ):
classifier = pickle.load(open(self.ab_path+'/Data/Pickles/subjective/classifier-MaxentClassifier.rotten.pickle', 'r'))
tokens = self.cl_helper.bag_of_words(self.cl_helper.extract_words(sentence ,is_stem = True))
decision = classifier.classify(tokens)
subj = classifier.prob_classify(tokens).prob('subjective')
obj = classifier.prob_classify(tokens).prob('objective')
print "Subjectivity = %s Objectivity = %s decision = %s" %(subj, obj ,decision)
if subj > obj:
return SUBJECTIVE
else:
return OBJECTIVE
def classify(self,sentence , classifier_names = "MaxentClassifier" , domain = "tweets"):
"""Classify the sentence
Keyword arguments:
classifier_names -- classifier names as space separated strings
for example if single classifier "MaxentClassifier",
if two classifiers MaxentClassifier NaiveBayes"
domain -- domain of the train corpus
:return NaiveBayesClassifier: Corresponding classifier
"""
pos = neg = 0
#NLTK Classifiers Starts Here ----------------------------------------->
results = []
req_classifiers = classifier_names.split();
for classifier_name in req_classifiers:
if not classifier_name == "WSD-SentiWordNet" :
pickled_classifier = 'classifier-%s.%s.pickle' % (classifier_name, domain)
pickle_dir = self.ab_path +'/'+'Data/Pickles/%s/%s' % (domain ,pickled_classifier)
if not os.path.exists(pickle_dir):
continue
classifier = pickle.load(open(pickle_dir, 'r'))
tokens = self.cl_helper.bag_of_words(self.cl_helper.extract_words(sentence))
#decision = classifier.classify(tokens)
neg = classifier.prob_classify(tokens).prob('neg')
pos = classifier.prob_classify(tokens).prob('pos')
decision = self.prepare_results(pos, neg, classifier_name, domain)
results.append({
"classifier" : classifier_name,
"result" : decision,
"pos_score" : pos,
"neg_score" : neg
})
#WSD Hue Starts Here ----------------------------------------->
if classifier_name == "WSD-SentiWordNet":
r = re.compile("[,.?()\\d]+ *")
lines_list = r.split(sentence)
pos, neg = self.wsd_helper.call_classifier(lines_list)
normalize_wsd = pos + neg + 1
pos = pos/normalize_wsd
neg = neg/normalize_wsd
print "Results from WSD SentiWordNet on %s Corpus "%domain
decision = self.prepare_results(pos, neg, classifier_name, domain)
results.append({
"classifier" : classifier_name,
"result" : decision,
"pos_score" : pos,
"neg_score" : neg
})
#WSD Hue ENDS Here ----------------------------------------->
return results
def prepare_results(self ,pos, neg, classifier_name ,domain):
print "Results from %s on %s Corpus" % (classifier_name, domain)
if abs(pos - neg) <= 0.15 and neg != 0 and pos != 0:
print "Text is Neutral/Hard To Classify"
print 'Positive = %s , Negative = %s' % (pos, neg)
return HARD_TO_CLASSIFY
elif pos > neg:
print " Text is POSITIVE"
print 'Positive = %s Negative = %s' % (pos, neg)
return POSITIVE
else:
print " Text is NEGATIVE"
print 'Positive = %s Negative = %s' % (pos, neg)
return NEGATIVE
class SentimentClassifier():
'''
classdocs
'''
def __init__(self ,classifier_names = "MaxentClassifier" , domains = "tweets"):
'''
Constructor
'''
self.cl_helper = ClassifierHelper()
self.wsd_helper = WSDHelper()
self.ab_path = os.path.dirname(os.path.abspath(__file__))
self.loaded_classifiers = self.set_classifiers(classifier_names, domains)
self.subjective_classifier = pickle.load(open(self.ab_path+'/Data/Pickles/subjective/classifier-MaxentClassifier.rotten.pickle', 'r'))
def subjective_and_objective_classification(self ,sentence ):
tokens = self.cl_helper.bag_of_words(self.cl_helper.extract_words(sentence ,is_stem = True))
decision = self.subjective_classifier.classify(tokens)
subj = self.subjective_classifier.prob_classify(tokens).prob('subjective')
obj = self.subjective_classifier.prob_classify(tokens).prob('objective')
print "Subjectivity = %s Objectivity = %s decision = %s" %(subj, obj ,decision)
if subj > obj:
return SUBJECTIVE
else:
return OBJECTIVE
def textblob_results(self ,sentence):
testimonial = TextBlob(sentence)
subjectivtiy = SUBJECTIVE
if testimonial.subjectivity > 0.3:
subjectivtiy = SUBJECTIVE
else:
subjectivtiy = OBJECTIVE
print "Subjectivity from Textblob %s" %(testimonial.subjectivity )
textblob_decision = self.prepare_textblob_results(testimonial.polarity)
return (subjectivtiy , textblob_decision)
def classify(self,sentence , classifier_names = "MaxentClassifier" , domain = "tweets"):
"""Classify the sentence
Keyword arguments:
classifier_names -- classifier names as space separated strings
for example if single classifier "MaxentClassifier",
if two classifiers MaxentClassifier NaiveBayes"
domain -- domain of the train corpus
:return NaiveBayesClassifier: Corresponding classifier
"""
pos = neg = 0
#NLTK Classifiers Starts Here ----------------------------------------->
results = []
req_classifiers = classifier_names.split();
for classifier_name in req_classifiers:
if not classifier_name == "WSD-SentiWordNet" :
key = classifier_name+"_"+domain
try:
classifier = self.loaded_classifiers[key]
except KeyError:
# Key is not present
classifier = self.load_classifiers(classifier_name, domain)
pass
tokens = self.cl_helper.bag_of_words(self.cl_helper.extract_words(sentence))
#decision = classifier.classify(tokens)
neg = classifier.prob_classify(tokens).prob('neg')
pos = classifier.prob_classify(tokens).prob('pos')
decision = self.prepare_results(pos, neg, classifier_name, domain)
results.append({
"classifier" : classifier_name,
"result" : decision,
"pos_score" : pos,
"neg_score" : neg
})
#WSD Hue Starts Here ----------------------------------------->
if classifier_name == "WSD-SentiWordNet":
r = re.compile("[,.?()\\d]+ *")
lines_list = r.split(sentence)
pos, neg = self.wsd_helper.call_classifier(lines_list)
normalize_wsd = pos + neg + 1
pos = pos/normalize_wsd
neg = neg/normalize_wsd
#print abs(pos - neg)
#print "Results from WSD SentiWordNet on %s Corpus "%domain
decision = self.prepare_results(pos, neg, classifier_name, domain)
results.append({
"classifier" : classifier_name,
"result" : decision,
"pos_score" : pos,
"neg_score" : neg
})
#WSD Hue ENDS Here ----------------------------------------->
return results
def prepare_results(self ,pos, neg, classifier_name ,domain):
print "Results from %s on %s Corpus" % (classifier_name, domain)
if abs(pos - neg) <= HARD_THRESHOLD:# and neg is not 0 and pos is not 0:
print " Text is Neutral/Hard To Classify"
print ' Positive = %s , Negative = %s' % (pos, neg)
return HARD_TO_CLASSIFY
elif pos > neg:
print " Text is POSITIVE"
print ' Positive = %s Negative = %s' % (pos, neg)
return POSITIVE
elif pos < neg:
print " Text is NEGATIVE"
print ' Positive = %s Negative = %s' % (pos, neg)
return NEGATIVE
else:
return HARD_TO_CLASSIFY
def prepare_textblob_results(self , textblob_polarity):
print "Results from TextBlob pattern"
polarity = textblob_polarity
print ' Polarity %s' % polarity
if -0.25 <= polarity <= 0.25:
print " Text is Neutral/Hard To Classify"
return HARD_TO_CLASSIFY
elif -0.25 > polarity:
print " Text is NEGATIVE"
return NEGATIVE
elif polarity > 0.25:
print " Text is POSITIVE"
return POSITIVE
else:
return HARD_TO_CLASSIFY
def load_classifiers(self ,classifier_name = "MaxentClassifier" , domain_name = "tweets"):
pickled_classifier = 'classifier-%s.%s.pickle' % (classifier_name, domain_name)
pickle_dir = self.ab_path +'/'+'Data/Pickles/%s/%s' % (domain_name ,pickled_classifier)
if not os.path.exists(pickle_dir):
return None
classifier = pickle.load(open(pickle_dir, 'r'))
return classifier
def set_classifiers(self ,classifier_names = "MaxentClassifier" , domains = "tweets"):
print "Loading classfiers..."
req_classifiers = classifier_names.split()
req_domains = domains.split()
loaded_classifiers = {}
for classifier_name in req_classifiers:
if classifier_name == "WSD-SentiWordNet":
continue
for domain_name in req_domains:
classifeir = self.load_classifiers(classifier_name, domain_name)
if classifeir is None:
continue
classifier_key_name = classifier_name+"_"+domain_name
loaded_classifiers[classifier_key_name] = classifeir
print "Classifier %s loaded!..." %(classifier_key_name)
return loaded_classifiers
