INTELLIGENT AUDIENCE EMOTIONAL STATE RECOGNITION SYSTEM
DOI:
https://doi.org/10.18372/2310-5461.65.19926Keywords:
neural networks, emotional analysis, emotion recognition, machine learning, real-time analysis, adaptive systems, feedback, deep learning, computer vision, natural language processing, multimodal analysis, affective computingAbstract
The integration of neural network analysis of audience emotional state and adaptive feedback mechanisms represents an innovative research direction, especially considering that existing user interaction systems often fail to account for the dynamics of emotional states in real-time. The proposed approach, based on neural networks, focuses on comprehensive analysis of emotional manifestations and their interconnections, providing deeper understanding of audience reactions and improving the effectiveness of adaptive response.
To ensure accurate tracking and analysis of audience emotional state, real-time data processing systems are being developed. Such systems find wide application across various fields, including educational processes, marketing research, customer service systems, and other areas where adaptive user interaction through different communication channels is critically important. The significance of these systems continues to grow, reflecting society's increasing needs for more personalized and emotionally sensitive interaction with digital systems.
The article examines fundamental aspects of neural network analysis, emotion recognition technologies, and adaptive feedback mechanisms. The primary focus of the research is the creation of a comprehensive emotional state analysis system using modern neural network technologies. The paper details the methodological foundations and technical aspects of implementing the proposed approach.
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