@amy.dj wrote:

I’m building a sentiment analysis program in python using Keras Sequential model for deep learning

my data is 20,000 tweets:

positive tweets: 9152 tweets
negative tweets: 10849 tweets

I wrote a sequential model script to make the binary classification

model=Sequential()
model.add(Embedding(vocab_size, 100, input_length=max_words))
model.add(Conv1D(filters=32, kernel_size=3, padding=‘same’, activation=‘relu’))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(250, activation=‘relu’))
model.add(Dense(1, activation=‘sigmoid’))
model.compile(loss=‘binary_crossentropy’, optimizer=‘adam’, metrics=[‘accuracy’])

Fit the model

print(model.summary())
history=model.fit(X_train[train], y1[train], validation_split=0.30,epochs=2, batch_size=128,verbose=2)

however I get very strange results! The model accuracy is almost perfect (>90) whereas the validation accuracy is very low (<1) (shown bellow)

Train on 9417 samples, validate on 4036 samples
Epoch 1/2

• 13s - loss: 0.5478 - acc: 0.7133 - val_loss: 3.6157 - val_acc: 0.0243
  Epoch 2/2
• 11s - loss: 0.2287 - acc: 0.8995 - val_loss: 5.4746 - val_acc: 0.0339

I tried to increase the number of epoch, and it only increases the model accuracy and lowers the validation accuracy

Any advice on how to overcome this issue?

Thank you!

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