Challenges and Opportunities of Text-Based Emotion Detection: A Survey
Abdullah Al Maruf, Fahima Khanam, Md. Mahmudul Haque, Zakaria Masud Jiyad, M. F. Mridha, Zeyar Aung
Abstract
Emotion detection has become an intriguing issue for researchers because of its psychological, social, and commercial significance. People express their feelings directly or indirectly through facial expressions, language, writing, or behavior. An emotion detection tool is a critical and practical way of recognizing and categorizing moods with various applications. Artificial intelligence is often used in research to identify emotions. Machine learning and deep learning algorithms produce high-quality solutions for diagnosing emotional diseases in social media users. Numerous studies and survey articles have been published on emotion detection based on textual data. However, most of these studies did not comprehensively address emerging architectures and performance analysis in emotion detection. This paper provides an extensive survey of state-of-the-art systems, techniques, and datasets for textual emotion recognition. Another goal of this study is to emphasize the limitations and provide up-and-coming research directions to fill these gaps in this rapidly evolving field. This survey paper investigated the concepts and the performances of different categories of textual emotion detection models, approaches, and methodologies.