A Deep Neural Network Model for the Detection and Classification of Emotions from Textual Content

Algorithm 1

Emotion classification through Bi-LSTM neural network.

	Data: Emotion dataset “M”, Train Set “TAS”, Test Set “TSS”
Result: Review Text Label: “J-S”, “F-S”, “F-G”
Start
// Review Text Encoding toward machine understandable word vectors (real valued)
while each review text RM do
while each word TM do
(1)	Word(token) indices allocation
End while
End while
Initializing Hyperparameter
(2)	embed_dim = 100, 128,300, max_features = 2000, epochs = 7, batch_size = 32, train set = 90%, test size = 10%
//Deep Learning model training
while each review text R MTASdo
(3)	Generate all word embedding vectors in R = [r1, r2, r3, …., rn]
(4)	Implement Bi-LSTM operation exploiting equations (1)–(13)
End while
// Allocating a label to Review Text final depiction
while each Review Text R MTSSdo
(5)	Trained(learned) model is built
(6)	Employ a softmax classifier using Eq. 14, for the classification of output obtained from the Bi-LSTM into “J-S”, “F-S”, “F-G”
End while
End