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.

• Preface xviiPadmesh TRIPATHI, Mritunjay RAI and Seifedine KADRYIntroduction xxiPadmesh TRIPATHI, Mritunjay RAI and Seifedine KADRYChapter 1. Advancements in Affective Computing: A Deep Learning Perspective on Sentiment Analysis and Emotion Detection 1R. Kishore KANNA, Priyanka SINGH, S. RAJU and Ayodeji Olalekan SALAU1.1. Introduction 21.2. Deep learning architectures for sentiment and emotion analysis 41.3. Feature extraction across modalities 51.4. Fusion techniques textual datasets 61.5. Textual datasets 81.6. Result analysis 111.7. Discussion 161.8. Conclusion 201.9. References 21Chapter 2. A Study on how Humanization of AI can Strengthen Emotional Intelligence in Hotel Operations: National and International Perspectives Through an ISM Approach 25Sushma SHARMA, Pawan KUMAR and Shubhangi TYAGI2.1. Introduction 262.2. AI and EI: national and international perspective 272.3. Identification of variables 282.4. Research gap 302.5. Research methodology 302.6. Analysis and interpretation 312.7. Discussion and recommendations 372.8. Conclusion 382.9. References 39Chapter 3. A Multimodal Fusion Framework Using a Deep Learning Approach for Identification of Visual Emotion and Sentiment Analysis 43Manisha, Pridhi ARORA, Padmesh TRIPATHI, Preeti KATIYAR and Mritunjay RAI3.1. Introduction 433.2. Related work 453.3. Hybrid fusion architecture for multimodal visual and textual sentiment analysis 473.4. Hybrid attention-based model architecture 523.5. Dataset design and preprocessing 613.6. Model training and optimization 623.7 Conclusion 643.8. References 64Chapter 4. Understanding Employee Sentiments: Leveraging Sentiment Analysis for Workplace Insights 69Salini ROSALINE and Muskan JAIN4.1. Introduction 694.2. The importance of capturing employee sentiments 714.3. Process of sentiment analysis in organizations 734.4. Tools for sentiment analysis in HR 794.5. Case study: using Orange software for sentiment analysis 824.6. Benefits and challenges of sentiment analysis in HR 954.7. Future directions and emerging trends 974.8. References 99Chapter 5. Psychological Foundations of Human Emotion and its Relevance to AI 105Roshitha Ratna MALLELA, B. Naresh KUMAR, Y. SUDHAMINI and G. Sriker REDDY5.1. Introduction 1055.2. Objectives of the chapter 1075.3. Psychological theories of emotion 1075.4. Physiological and neurological perspectives 1125.5. Psychological theory comparison within the field of AI design 1145.6. Verbal and textual indicators 1175.7. Use of language and sentiment analysis 1175.8. Psycholinguistic features, emotional valence 1175.9. Prosody and SER 1175.10. Culture and background information 1185.11. Individual and cultural variations 118Contents ix5.12. Variations in people and populations 1195.13. Emotional norms and cultural influences 1195.14. Difficulties in universal emotion recognition 1205.15. Bridging psychology and AI in emotion detection 1215.16. How AI models interpret human emotions 1215.17. Emotion recognition using ML methods 1225.18. Sentiment analysis using NLP 1225.19. Facial and gesture analysis – computer vision 1235.20. Psychological principles in AI design 1245.21. Case studies and applications 1285.22. Human–computer interaction 1315.23. Emerging trends, open questions and ethical considerations in emotion AI 1365.24. The ethical development of AI in emo-sensitive apps 1395.25. Conclusion 1405.26. References 142Chapter 6. Machine Learning Approaches for Sentiment Analysis 155T.C. Swetha PRIYA and A. Kanaka DURGA6.1. Introduction 1566.2. Fundamentals of sentiment analysis 1576.3. Introduction to machine learning 1606.4. Sentiment analysis using machine learning 1616.5. Comparative analysis of traditional machine learning, deep learning and LLM approaches 1736.6. Applications of sentiment analysis 1746.7. Conclusion 1756.8. References 176Chapter 7. Natural Language Processing for Sentiment Analysis 179P.R. ANISHA, Umaima Qader MOHIUDDIN and Hajira FAROOQUI7.1. Introduction 1807.2. Background and related work 1827.3. LLMs for sentiment analysis 1837.4. Applications in financial and economic domains 1867.5. Case study: Financial and economics with LLMs 1887.6. Evaluation metrics 1957.7. Challenges and limitations 1967.8. Conclusion 1967.9. Results 1977.10. Future scope 2007.11. References 200Chapter 8. Decoding Human Affect: A Structural Equation Modeling Approach to Deep Learning-Based Emotional and Sentiment Analysis 203Remmiya Rajan P., Kolapo IGE and Dineshan E.8.1. Introduction 2038.2. Study rationale 2058.3. Study objectives 2078.4. Study hypotheses 2078.5. Review of related literature 2088.6. Study methodology 2128.7. Analysis and interpretation 2158.8. Study findings 2278.9. Conclusion 2288.10. References 228Chapter 9. AI in Analyzing Emotions on Social Media Platforms: A Paradigm Shift 231Md. Saddam HOSSAIN, Farhana YEASMIN and Md. Shajahan KABIR9.1. Introduction 2319.2. Theoretical framework: affective computing and emotion recognition 2349.3. Proposed conceptual framework 2369.4. Characteristics and challenges of social media data 2379.5. AI methodologies for emotion detection on social media 2399.6. Applications and case studies 2409.7. Ethical considerations in AI-based sentiment analysis 2439.8. Conclusion and future directions 2449.9. References 248Chapter 10. Human Emotion Recognition Using Speech Signals 253Geetanjali SRIVASTAVA, Girish SURSAKAR, Akash VISHWAKARMA and Priyanka JAIN10.1. Introduction 25410.2. Related work 25510.3. Methodology 25810.4. Experimental results and discussions 26910.5. Forensic applications and behavioral surveillance 27110.6. Conclusion 27310.7. References 273Chapter 11. Transforming Employee Relations: The Role of Speech Emotion Recognition in Modern HR Practices 281Revati Ramrao RAUTRAO11.1. Introduction 28211.2. Background 29411.3. Focus of the chapter 29511.4. Conclusion 29811.5. References 299Chapter 12. Fuzzy and Neutrosophic Logic-Based Models for Handling Uncertainty in Emotion Detection 303Ajoy Kanti DAS, Nandini GUPTA, Suman PATRA and Takaaki FUJITA12.1. Introduction 30312.2. Basics of fuzzy and neutrosophic logic 30912.3. Understanding uncertainty in emotions 31112.4. Fuzzy logic-based emotion detection models 31412.5. Neutrosophic logic-based emotion detection models 31812.6. Comparison with classical methods. 32112.7. Applications in real life 32312.8. Challenges and integration with modern AI 32612.9. Conclusion 32912.10. References 331Chapter 13. The Evolution of Emotion Research: From Darwin’s Theories to AI 337Sirine Hadjer ZAABTA, Omar MATARI and Omar SEBBAGH13.1. Introduction 33813.2. Evolutionary foundations of emotion research 33913.3. From marginalization to cognitive integration 34613.4. The rise of emotional neuroscience 34813.5. Toward affective computing and AI integration 35213.6. HGEAIF: bridging psychological theory and modern computation 35713.7. Ethical and conceptual challenges 36013.8. Conclusion 36213.9. References 363Chapter 14. Real-Time Face Emotion Detection using Mobilenetv1 and Speech Emotion Recognition using Multimodal Analysis 385Budhaditya BHATTACHARYYA14.1. Introduction 38514.2. Technical specifications 38614.3. Speech emotion recognition 38714.4. Data preprocessing for audio analysis 39014.5. Simulation results 39214.6. Conclusion 40314.7. References 404List of Authors 407Index 411