Emotion Analysis

Emotion analysis in natural language processing (NLP) aims to computationally understand and categorize emotions expressed in text, speech, and visual media. Current research focuses on improving the accuracy and robustness of emotion detection across diverse languages and modalities, employing techniques like large language models (LLMs), multimodal fusion, and transformer-based architectures, often informed by psychological theories of emotion. This field is significant for its potential applications in various domains, including mental health monitoring, human-computer interaction, and social media analysis, as well as for advancing our understanding of human emotion itself. Challenges remain in addressing cultural biases and the inherent subjectivity of emotion perception.