85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
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import numpy as np
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import pandas as pd
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from sklearn.feature_extraction.text import CountVectorizer
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from keras.preprocessing.text import Tokenizer
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from keras.preprocessing.sequence import pad_sequences
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from keras.models import Sequential
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from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
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from sklearn.model_selection import train_test_split
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from keras.utils.np_utils import to_categorical
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import re
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'''
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Task 3: playing with NN framwork/keras and basic sentiment analysis
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- use the following model as a baseline and improve it!
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- export your metadata (just basic hyperparameters and outcomes for test data!)
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- test data = 0.3 (not in this example, change it!)
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- random_state = 4222
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- no need to cross-validation!
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'''
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# parameters
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max_fatures = 500
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embed_dim = 128
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lstm_out = 196
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dropout = 0.1
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dropout_1d = 0.4
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recurrent_dropout = 0.1
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random_state = 1324
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validation_size = 1000
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batch_size = 16
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epochs=2
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verbose= 2
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df = pd.read_csv('dataset_sentiment.csv')
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df = df[['text','sentiment']]
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print(df[0:10])
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df = df[df.sentiment != "Neutral"]
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df['text'] = df['text'].apply(lambda x: x.lower())
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df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))
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df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\s]','',x)))
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tok = Tokenizer(num_words=max_fatures, split=' ')
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tok.fit_on_texts(df['text'].values)
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X = tok.texts_to_sequences(df['text'].values)
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X = pad_sequences(X)
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nn = Sequential()
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nn.add(Embedding(max_fatures, embed_dim, input_length = X.shape[1]))
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nn.add(SpatialDropout1D(dropout_1d))
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nn.add(LSTM(lstm_out, dropout=dropout, recurrent_dropout=recurrent_dropout))
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nn.add(Dense(2, activation='softmax'))
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nn.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics = ['accuracy'])
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print(nn.summary())
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Y = pd.get_dummies(df['sentiment']).values
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.30, random_state = random_state)
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nn.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, verbose=verbose)
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X_validate = X_test[-validation_size:]
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Y_validate = Y_test[-validation_size:]
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X_test = X_test[:-validation_size]
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Y_test = Y_test[:-validation_size]
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score, accuracy = nn.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)
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print("score: %.2f" % (score))
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print("acc: %.2f" % (accuracy))
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pos_cnt, neg_cnt, pos_ok, neg_ok = 0, 0, 0, 0
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for x in range(len(X_validate)):
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result = nn.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]
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if np.argmax(result) == np.argmax(Y_validate[x]):
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if np.argmax(Y_validate[x]) == 0: neg_ok += 1
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else: pos_ok += 1
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if np.argmax(Y_validate[x]) == 0: neg_cnt += 1
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else: pos_cnt += 1
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print("pos_acc", pos_ok/pos_cnt*100, "%")
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print("neg_acc", neg_ok/neg_cnt*100, "%")
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X2 = ['what are u going to say about that? the truth, wassock?!']
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X2 = tok.texts_to_sequences(X2)
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X2 = pad_sequences(X2, maxlen=26, dtype='int32', value=0)
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print(X2)
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print(nn.predict(X2, batch_size=1, verbose = 2)[0])
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