def election(self): self.election_classifier = NLPElection( self.classifier, self.MNB_classifier, self.BernoulliNB_classifier, self.LogisticRegression_classifier, self.LinearSVC )
class Sentiment:
'''
General purpose sentiment analyser. It relies on pickled
training and testing sets as well as pretrained classifiers
to work.
'''
def __init__(self, path):
word_features5k_f = open(path, "rb")
self.word_features = pickle.load(word_features5k_f)
word_features5k_f.close()
def train(self, nltk, mnb, bnb, lgr, svc):
self.nltk_classifier_nb(nltk)
self.multinomial_nb(mnb)
self.bernoulli_nb(bnb)
self.logistic_regress(lgr)
self.linear_svc(svc)
self.election()
def find_features(self, document):
words = word_tokenize(document)
features = {}
for word in self.word_features:
features[word] = (word in words)
return features
def create_sets(self, path, boundary=60):
'''
This method is for creating training and testing
sets for your classifiers. It actually has no use
for this module, but serves as a reference
'''
featuresets_f = open(path, "rb")
self.feature_sets = pickle.load(featuresets_f)
featuresets_f.close()
random.shuffle(self.feature_sets)
length = len(self.feature_sets)
limit = length * boundary / 100
self.training_set = feature_sets[:limit]
self.testing_set = feature_sets[limit:]
def nltk_classifier_nb(self, path):
open_file = open(path, "rb")
self.classifier = pickle.load(open_file)
open_file.close()
def multinomial_nb(self, path):
open_file = open(path, "rb")
self.MNB_classifier = pickle.load(open_file)
open_file.close()
def bernoulli_nb(self, path):
open_file = open(path, "rb")
self.BernoulliNB_classifier = pickle.load(open_file)
open_file.close()
def logistic_regress(self, path):
open_file = open(path, "rb")
self.LogisticRegression_classifier = pickle.load(open_file)
open_file.close()
def linear_svc(self, path):
open_file = open(path, "rb")
self.LinearSVC = pickle.load(open_file)
open_file.close()
def election(self):
self.election_classifier = NLPElection(
self.classifier,
self.MNB_classifier,
self.BernoulliNB_classifier,
self.LogisticRegression_classifier,
self.LinearSVC
)
def analyse(self, data, lower_limit):
'''
Performs the sentiment analysis for a list of text data
'''
self.sentiments = []
for text in data:
features = self.find_features(text)
if self.election_classifier.unanimity(features) < lower_limit:
self.sentiments.append(self.BernoulliNB_classifier.classify(features))
else:
self.sentiments.append(self.election_classifier.vote(features))
def get_sentiment(self, data, lower_limit=0.4):
'''
Used for deriving sentiment on a list of data around
a particular topic, such as stock trends or tweets
'''
self.analyse(data, lower_limit)
total = len(self.sentiments)
proportion = self.sentiments.count('pos') / total
if proportion > 0.75:
return 'very good'
elif proportion > 0.5:
return 'good'
elif proportion > 0.25:
return 'bad'
else:
return 'very bad'
def get_sentiment_single(self, text):
'''
Used for deriving sentiment on a single body of text,
such as a review or critique
'''
features = self.find_features(text)
if self.election_classifier.unanimity(features) > 0.75:
if self.election_classifier.vote(features) == 'pos':
return 'very good'
else:
return 'very bad'
else:
if self.election_classifier.vote(features) == 'pos':
return 'good'
else:
return 'bad'
