A novel intervention method for aspect-based emotion Using Exponential Linear Unit (ELU) activation function in a Deep Neural Network

A novel intervention method for aspect-based emotion Using Exponential Linear Unit (ELU) activation function in a Deep Neural Network

Abstract:

Sentimental analysis, a most opinion analyzer which takes a process of mathematical calculation for fixing the exact emotion, object or user expected reviews, etc. In the modern generation everything comes in a digital way where sales and buyers are increased in a rapid manner through the internet only. Not only commercial access but also education in this pandemic situation 2021, takes a major part in worldwide internet. In this way sentimental analysis has accounted for its NLP framework in the feedback process also. Detection through many algorithms using machine learning, computer vision and deep learning are in trend now. In such views, a Novel intervention aspect-based (NIAB) sentimental analysis is proposed to classify the emotion from twitter dataset. By using an activation function, the output of each node is identified accurately. Neural network has many input and output nodes that are interconnected through n-number of hidden layers. These nodes are determined through mapping the dependent variable. For a clear direction of slope an non-linear activation function is introduced called an Exponential Linear Unit (ELU) that has the highest scale with positive value. The exact range between the input and output value is 0.1 and 0.3 which has better output compared to Rectified linear unit (RELU).