A Review of Emotion Recognition using Machine Learning

Abstract

Artificial intelligence and machine learning study emotion detection. The rapid advancement of information technology and sensors has enabled machines to understand and evaluate human emotions. With the help of applications in various fields, including healthcare, marketing, and psychology, emotion detection can be achieved by scrutinizing physiological signals, speech, behaviour, or facial expressions. Assisting affective computing by accurately recognizing human emotions is one way to make progress. The goal of this research is to present an in-depth analysis of how machine learning can be used to detect emotion. At the start of the research, a review of emotion detection will be presented, and its significance in several sectors will be discussed. Different approaches to emotion recognition, such as deep, supervised, unsupervised, and ensemble learning, will be thoroughly analyzed. Furthermore, the study will examine the obstacles to generalization and data availability in emotion recognition through machine learning. In addition to discussing the moral implications of emotion-sensing technology, the research will examine the ethical implications. Ultimately, this research will provide important insight into the capability of machine learning to recognize emotions and its potential benefits and drawbacks. The results of this analysis will be interesting to scientists, professionals, and decision-makers developing and using emotion detection equipment based on machine-learning techniques.