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Exploration of the Theory and Application of Artificial Intelligence in Emotion Recognition

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DOI: 10.23977/jaip.2024.070217 | Downloads: 8 | Views: 145


Zhang Tiantian 1


1 Hangzhou Lead Sail Technology Co., Ltd., Hangzhou, Zhejiang, 310020, China

Corresponding Author

Zhang Tiantian


This paper comprehensively explores the theoretical foundations and practical applications of artificial intelligence in the field of emotion recognition, emphasizing the importance of improving the accuracy and real-time capabilities of emotion recognition through advanced technology. The global demand for efficient emotion recognition technology is growing, especially in handling complex data related to human emotions, where AI shows unique potential. The article begins with the diverse definitions and classifications of emotions, covering psychological and physiological perspectives, and introduces cross-cultural comparisons to explain the diversity of emotions. It also compares traditional and modern emotion measurement techniques, highlighting their limitations and controversies, thus providing theoretical support for the application of AI technology. Particularly in the fields of machine learning and deep learning, through specific cases such as CNNs and RNNs, the effectiveness of these technologies in text, audio, and video emotion analysis is demonstrated. Additionally, this paper discusses the practical applications of emotion recognition technology in commercial services, healthcare, and public safety, as well as the ethical and legal challenges it faces. This research aims to outline future development trends in emotion recognition technology, emphasizing the importance of interdisciplinary cooperation and the need for technological innovation, providing direction and insights for future research and applications.


Artificial Intelligence, Emotion Recognition, Emotion Analysis, Machine Learning


Zhang Tiantian, Exploration of the Theory and Application of Artificial Intelligence in Emotion Recognition. Journal of Artificial Intelligence Practice (2024) Vol. 7: 129-140. DOI:


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