Table 3.

Details of hyperparameters used for the proposed CNN model.

Parameter name	Value	Description
Max features	20,000	Vocabulary size of the embedding layer.
Embedding dim	50	Embedding dimension.
Dropout	0.2	Dropout rate
Number of filters	250	Number of filters in the convolution layer.
Kernel size	3	Kernel size in the convolution layer.
Activation function	Relu	Activation function of the convolution layer
Dense	250	Number of neurons in the hidden layer.
Loss function	Categorical cross entropy	Loss function of the output layer.
Optimizer	Adam	Optimization algorithm of the model.