Optimized Human-Emotion Detection in Written-Text using Hybrid Machine Learning Classification Algorithm

Abstract

No part of our psychological life is more essential to the quality and significance of our reality than emotions. In psychology, Emotion is often defined as a complex state of feeling that results in physical and psychological changes that influence thought and behavior. Emotionality is associated with a range of psychological phenomena, including temperament, personality, mood, and motivation. In 1972, psychologist Paul Eckman suggested that six basic emotions are universal throughout human cultures: fear, disgust, anger, surprise, happiness, and sadness. Emotion Recognition is an important area of work to improve the interaction between humans and machines. Emotion Detection will play a promising role in the field of Artificial Intelligence, especially in the case of Human-Machine Interface Development, Human-Computer Interaction (HCI), User-Experience (UX), and Designs. In our study case, we went through the vast area of Emotion Recognition and Detection from an AI and ML perspective, in which different parameters were taken into consideration. In this work, through our research, we developed a Human-Emotion Detection methodology based on Written-Text using a preprocessing technique based on meaningless stop words removal and a Hybrid-ML Algorithm, which is made of a Naïve-Bayes Classifier (NBC) and a Convolutional Neural Network (CNN) for a better accuracy alongside with an Optimized Text- Analysis method for Preprocessing. The preprocessing is built up around many different techniques that help the data to be reliable, standardized, and clean. It all started with the stop word removal which is one of the key parts of our work, then the standardization of the data and the following part was tokenization, followed by the TF-IDF Vectorization which was applied and we finished by a vocabulary construction.