def test_sentiment_1(self): text = "Không tin tưởng vào ngân hàng BIDV " actual = sentiment(text) expected = "TRADEMARK#NEGATIVE" self.assertEquals(actual[0], expected)
def test_sentiment_2(self): text = "Bạn ra ngân hàng hỏi luôn cho nhanh giải đáp qua đây ko ăn thua lắm." actual = sentiment(text) expected = "CUSTOMER SUPPORT#NEGATIVE" self.assertEquals(actual[0], expected)
nineties_score = nineties['Lyrics'] nineties_score = nineties_score.to_string() nineties_score = ''.join(nineties_score) thousands_score = thousands['Lyrics'] thousands_score = thousands_score.to_string() thousands_score = ''.join(thousands_score) tens_score = tens['Lyrics'] tens_score = tens_score.to_string() tens_score = ''.join(tens_score) #print(file) from model import sentiment func = sentiment() six_data = func.tokener(sixties_score) seven_data = func.tokener(seventies_score) eight_data = func.tokener(eighties_score) nine_data = func.tokener(nineties_score) thousand_data = func.tokener(thousands_score) ten_data = func.tokener(tens_score) data = {'Year': ['1960s', '1970s', '1980s', '1990s', '2000s', '2010s'], 'Sentiment Score': [six_data, seven_data, eight_data, nine_data, thousand_data, ten_data] }
from os.path import join, dirname import sys from languageflow.board import Board from languageflow.log import MultilabelLogger from languageflow.log.tfidf import TfidfLogger from load_data import load_dataset from model import sentiment data_file = join(dirname(dirname(dirname(dirname(__file__)))), "data", "fb_bank", "corpus", "test.xlsx") X_test, y_test = load_dataset(data_file) y_test = [tuple(item) for item in y_test] y_pred = sentiment(X_test) log_folder = join(dirname(__file__), "analyze") model_folder = join(dirname(__file__), "model") board = Board(log_folder=log_folder) MultilabelLogger.log(X_test, y_test, y_pred, log_folder=log_folder) TfidfLogger.log(model_folder=model_folder, log_folder=log_folder) board.serve(port=62010)
def test_sentiment(self): sentence = "Thật tuyệt vời" tags = "POSITIVE" actual = sentiment(sentence) self.assertEquals(tags, actual[0])
import model as s print(s.sentiment("This movie was awesome! The acting was great, plot was wonderful")) print(s.sentiment("This movie was utter junk. I don't see what the point was at all. Horrible movie, 0/10"))
def test_sentiment_5(self): sentence = "Vietcombank là lũ lừa đảo" tags = "NEGATIVE" actual = sentiment(sentence) self.assertEquals(tags, actual[0])
def test_sentiment_3(self): text = "Tháng này tiền banking bị thu phí 3 lần liên tục đề nghị vietcombank xem xét lại " actual = sentiment(text) expected = "INTEREST RATE#NEGATIVE" self.assertEquals(actual[0], expected)
def test_sentiment_3(self): sentence = "Dịch vụ rắc rối." tags = "NEGATIVE" actual = sentiment(sentence) self.assertEquals(tags, actual[0])
def test_sentiment_4(self): sentence = "Thật sự mình rất hài lòng khi làm việc với VietcomBank" actual = sentiment(sentence) expected = "POSITIVE" self.assertEquals(expected, actual[0])
def test_sentiment_2(self): sentence = "Không tin tưởng vào ngân hàng BIDV." tags = "NEGATIVE" actual = sentiment(sentence) self.assertEquals(tags, actual[0])
def test_sentiment_1(self): sentence = "Nhân viên BIDV dễ thương nhiệt tình lắm ạ" tags = "POSITIVE" actual = sentiment(sentence) self.assertEquals(tags, actual[0])
config.word_num = len(SENTENCE.vocab) print("Labels:", LABEL.vocab.itos) print("Vocabulary Size: {}".format(config.words_num)) if args.resume_snapshot: if args.cuda: model = torch.load( args.resume_snapshot, map_location=lambda storage, location: storage.cuda(args.gpu)) else: model = torch.load(args.resume_snapshot, map_location=lambda storage, location: storage) else: if args.data == 'SST1': model = sentiment(config) if args.cuda: model.cuda() print("Shift model to GPU") parameter = filter(lambda p: p.requires_grad, model.parameters()) optimizer = torch.optim.Adadelta(parameter, lr=args.lr, weight_decay=args.weight_decay) criterion = nn.MarginRankingLoss() early_stop = False best_dev_acc = 0 iteration = 0 iters_not_improved = 0
def test_sentiment_3(self): text = "VCB Mobile B@bking. Chuyển rất nhanh. Giao diện đẹp, thân thiện. Dễ thao tác" actual = sentiment(text) expected = "INTERNET BANKING#POSITIVE" self.assertEquals(actual[0], expected)
def test_sentiment_7(self): sentence = "éo tin" tags = "NEGATIVE" actual = sentiment(sentence) self.assertEquals(tags, actual[0])
def test_sentiment(self): text = "Gọi mấy lần mà lúc nào cũng là các chuyên viên đang bận hết ạ " actual = sentiment(text) expected = "CUSTOMER SUPPORT#NEGATIVE" self.assertEquals(actual[0], expected)
def test_sentiment_1(self): text = "Chúc mừng VCB, luôn thành công và tạo niềm tin cho khách hàng an tâm khi đồng hành cùng VCB." actual = sentiment(text) expected = "TRADEMARK#POSITIVE" self.assertEquals(actual[0], expected)