Affiliation:
1. Institute of Innovation in Technology and Management, India
2. Maharaja Surajmal Institute, India
Abstract
Emotion detection using deep learning techniques has gained significant attention due to its wide-ranging applications in fields such as healthcare, marketing, human-computer interaction, and more. However, several challenges hinder the accurate detection and interpretation of emotions from various modalities such as text, speech, facial expressions, and physiological signals. This chapter systematically reviews the challenges faced in emotion detection and proposes innovative solutions leveraging deep learning methodologies. Through a combination of literature review, empirical analysis, and case studies, this chapter offers insights into overcoming these challenges and improving the performance and reliability of emotion detection systems across diverse applications.
Reference68 articles.
1. A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues
2. Text‐based emotion detection: Advances, challenges, and opportunities
3. Ahmed, S., Reyadh, A. S., Sithil, F. T., Shah, F. M., & Shaafi, A. I. (2020). An attention-based approach to detect emotion from tweets. 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE), 182–187.
4. Real-Time Emotion Recognition Using Deep Learning Methods: Systematic Review.;Intelligent Methods in Engineering Sciences,2023
5. Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions