AI Innovations in Human Emotion Detection and Sentiment Analysis

Human feelings, their simulation and the perception of them in the reality of the ever-changing environment of AI are some of the central ideas regarding the construction of intelligent, ethical and human related systems.

AI Innovations in Human Emotion Detection and Sentiment Analysis covers the field of computational emotion analysis, using a mix of machine learning, deep learning, causal modeling, multimodal fusion and soft computing. It examines speech, text, facial expression and processing of physiological cues. It also solves problems such as uncertainty management, cross-cultural generalization, class imbalance and domain adaptation. This book covers advanced architectures, such as transformer encoders, recurrent networks, attention and parameter efficient fine-tuning strategies, alongside explainability models and explainable AI. Both technical and emotion analytics are performed on organizational behavior, service innovation, human–AI synergy and Industry 5.0 prisms.

This book offers theorists, practitioners and graduate students a systematic approach to the creation of transparent, adaptive and socially responsible emotion-sensitive intelligent systems by integrating theoretical knowledge and practical experience and experimentation.

Padmesh Tripathi is a professor in the Department of Computer Science and Engineering at the Delhi Technical Campus, India. His research expertise covers inverse problems, AI, image processing and optimization.

Mritunjay Rai is an assistant professor in the Department of Electrical and Electronics Engineering at Shri Ramswaroop Memorial University, India. His research specializes in digital image processing, thermal imaging and AI.

Seifedine Kadry is a professor of data science at the Lebanese American University, Lebanon. His research specializes in AI, machine learning, cybersecurity and educational technology.