Table 6. Layered architecture of deep neural networks.

LSTM	CNN	CNN-LSTM
Embedding(5000,200)	Embedding(5000,200)	Embedding(5000,200)
Dropout(0.5)	Conv1D(128,5,activation=‘relu’)	Conv1D(128,5,activation=‘relu’)
LSTM(128)	MaxPooling1D(pool_size = 5)	MaxPooling1D(pool_size = 2)
Dropout(0.5)	Activation(‘relu’)	LSTM(64,return_sequences = True)
LSTM(64)	Dropout(rate = 0.5)	Conv1D(128,5,activation=‘relu’)
Dropout(0.5)	Flatten()	MaxPooling1D(pool_size = 2)
Dense(32)	Dense(32)	Flatten()
Dense(5)	Dense(5)	Dense(5)
loss =‘categorical_crossentropy’, optimizer =‘adam’, epochs = 100, batch_size = 128, activation=‘softmax’