class SentimentAnalysis:
def __init__(self):
self.tweet = ""
self.naivebayes = NaiveBayes(1)
self.maxint = MaximimEntropy()
self.svm = SVM()
self.algorithm = SelfAlgorithm()
pos, neg = self.algorithm.get_pos_neg_data()
self.preprocess = PreProcess(pos, neg)
return
def data_preprocessing(self, tweet):
self.tweet = self.preprocess.pre_process(tweet)
def predict_naive_bayes(self):
return self.naivebayes.analyse(self.tweet)
def predict_maximum_entropy(self):
return self.maxint.analyse(self.tweet)
def predict_svm(self):
return self.svm.predict(self.tweet)
def predict_self_algorithm(self):
return self.algorithm.predict(self.tweet)
def predict(self, tweet):
self.data_preprocessing(tweet)
nb = self.predict_naive_bayes()
mi = self.predict_maximum_entropy()
svm = self.predict_svm()
o = self.predict_self_algorithm()
# divider = '-' * (180)
# width = 10
# dict = {}
# dict["tweet"] = tweet.ljust(100)
# dict["naivebayes"] = self.label[nb].ljust(width)
# dict["maxint"] = self.label[mi].ljust(width)
# dict["svm"] = self.label[svm].ljust(width)
# dict["self"] = self.label[o].ljust(width)
# dict["divider"] = divider
# print divider
# print "%(tweet)s\t %(naivebayes)s %(maxint)s %(svm)s %(self)s" % dict
return nb, mi, svm, o
#s = SentimentAnalysis()
#print
#print
#s.predict("nooot Good")
#s.predict("that movie i watched wasn't so gooood")
#s.predict("i am preety good")
#s.predict("hari is a baaad booy")
#s.predict("abinash is a bad boy")
#s.predict("i don't like the way that pradeep likes :( :) :D")
#s.predict("i got a new laptop :D")
def __init__(self):
self.tweet = ""
self.naivebayes = NaiveBayes(1)
self.maxint = MaximimEntropy()
self.svm = SVM()
self.algorithm = SelfAlgorithm()
pos, neg = self.algorithm.get_pos_neg_data()
self.preprocess = PreProcess(pos, neg)
return