Advancements in Emotion Detection: A Comprehensive Review of Text and Audio-Based Approaches

Abstract

Abstract: Emotion detection from text and audio has garnered significant attention in recent years due to its applications in various domains such as mental health support, conversational agents, and stress detection. In this review, we analyze methodologies and results in this field. The papers cover a range of topics including machine learning models for emotion detection from speech, deep learning methods for text-based emotion classification, and the integration of AI in healthcare and education. Traditional machine learning algorithms such as logistic regression and SVM are compared with deep learning architectures like CNNs and RNNs/LSTMs for their effectiveness in emotion detection tasks. Spectral and acoustic features for speech emotion detection are examined alongside text-based emotion detection approaches utilizing lexicon-based methods and machine learning techniques. Furthermore, the impact of voice assistant personality on user attitudes and behavior is explored, shedding light on the importance of designing emotionally intelligent AI systems. Additionally, challenges and ethical considerations in deploying AI-driven solutions in mental health and healthcare settings are discussed. Through this comprehensive review, we provide insights into the current state of emotion detection research, highlighting trends, challenges, and future directions in the field