REVIEW AND ANALYSIS OF APPROACHES AND PRACTICAL APPLICATIONS OF HUMAN EMOTION RECOGNITION

Human emotions are complex and multifaceted, making them difficult to quantify and analyze. However, as technology advances, researchers are exploring the artificial intelligence used to better understand and classify human emotions. In particular, neural networks are becoming increasingly popular for emotion recognition and analysis because of their ability to learn and adapt from large datasets. Objective. This study aims to review and analyze different approaches and practical applications of recognizing human emotions using neural networks. In particular, the study focuses on examining neural networks different types used for emotion recognition, data collection methods, as well as emotion recognition practical applications in various fields. The study also aims to identify limitations and issues associated with emotion recognition using neural networks. Methods. This study used a comprehensive review of relevant literature, including scholarly articles, conference proceedings, and books, to gather information on approaches and practical applications of human emotion recognition using neural networks. The review focused on recent research. The information collected was analyzed to identify the neural networks different types used for emotion recognition and data collection methods. Results. The literature review revealed several approaches to emotion recognition using neural networks, including convolutional neural networks, recurrent neural networks, and hybrid neural networks. Practical applications of emotion recognition using neural networks are found in a variety of fields, including marketing, health care, and education. The review also identified limitations and challenges associated with emotion recognition using neural networks, including dataset bias and the need for more diverse and representative datasets. Conclusion. A review and analysis of the approaches and practical applications of human emotion recognition technology using neural networks highlight the potential benefits and challenges associated with this technology. The results of this study can be used to guide future research on emotion recognition using neural networks to improve the accuracy and applicability of emotion recognition in various fields.