def __init__( self ): self.driver = webdriver.Firefox() self.classifier = cf.classifier() self.URLs = [] self.contexts = [] self.bag = utils.load_dictionary() self.tagger = Mecab()
def __init__(self):
# initalize Mecab tagger
self.tagger = Mecab()
# initalize regular expression
self.exp = re.compile(self.POS, re.IGNORECASE)
# load sentiment dictionary
self.bag = utils.load_dictionary()
# load model if exist
with open("../Resources/models/model", "rb") as model_file:
self.model = pickle.load(model_file)
print("accuracy : {:.2f}%" .format(err))
print("TPR : {:.2f}%" .format(100*(tp/r_pos)))
print("TNR : {:.2f}%" .format(100*(tn/r_neg)))
if __name__ == "__main__":
global POS
# initalize Mecab tagger
tagger = Mecab()
# initalize regular expression
exp = re.compile(POS, re.IGNORECASE)
# load sentiment dictionary
bag = utils.load_dictionary()
# load model if exist
try:
with open("../Resources/models/model", "rb") as model_file:
model = pickle.load(model_file)
except IOError as err:
# load training reviews from file
train_review = utils.load_reviews("../Resources/samples/train_data")
# get feature from train data
train_data, train_label = feature_data(tagger, exp, bag, train_review)
# initalize classifer class
model = SVM()
# train model
model.train(train_data, train_label)
#save model
