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