Preface xvii
Padmesh TRIPATHI, Mritunjay RAI and Seifedine KADRY

Introduction xxi
Padmesh TRIPATHI, Mritunjay RAI and Seifedine KADRY

Chapter 1. Advancements in Affective Computing: A Deep Learning Perspective on Sentiment Analysis and Emotion Detection 1
R. Kishore KANNA, Priyanka SINGH, S. RAJU and Ayodeji Olalekan SALAU

1.1. Introduction 2
1.2. Deep learning architectures for sentiment and emotion analysis 4
1.3. Feature extraction across modalities 5
1.4. Fusion techniques textual datasets 6
1.5. Textual datasets 8
1.6. Result analysis 11
1.7. Discussion 16
1.8. Conclusion 20
1.9. References 21

Chapter 2. A Study on how Humanization of AI can Strengthen Emotional Intelligence in Hotel Operations: National and International Perspectives Through an ISM Approach 25
Sushma SHARMA, Pawan KUMAR and Shubhangi TYAGI

2.1. Introduction 26
2.2. AI and EI: national and international perspective 27
2.3. Identification of variables 28
2.4. Research gap 30
2.5. Research methodology 30
2.6. Analysis and interpretation 31
2.7. Discussion and recommendations 37
2.8. Conclusion 38
2.9. References 39

Chapter 3. A Multimodal Fusion Framework Using a Deep Learning Approach for Identification of Visual Emotion and Sentiment Analysis 43
Manisha, Pridhi ARORA, Padmesh TRIPATHI, Preeti KATIYAR and Mritunjay RAI

3.1. Introduction 43
3.2. Related work 45
3.3. Hybrid fusion architecture for multimodal visual and textual sentiment analysis 47
3.4. Hybrid attention-based model architecture 52
3.5. Dataset design and preprocessing 61
3.6. Model training and optimization 62
3.7 Conclusion 64
3.8. References 64

Chapter 4. Understanding Employee Sentiments: Leveraging Sentiment Analysis for Workplace Insights 69
Salini ROSALINE and Muskan JAIN

4.1. Introduction 69
4.2. The importance of capturing employee sentiments 71
4.3. Process of sentiment analysis in organizations 73
4.4. Tools for sentiment analysis in HR 79
4.5. Case study: using Orange software for sentiment analysis 82
4.6. Benefits and challenges of sentiment analysis in HR 95
4.7. Future directions and emerging trends 97
4.8. References 99

Chapter 5. Psychological Foundations of Human Emotion and its Relevance to AI 105
Roshitha Ratna MALLELA, B. Naresh KUMAR, Y. SUDHAMINI and G. Sriker REDDY

5.1. Introduction 105
5.2. Objectives of the chapter 107
5.3. Psychological theories of emotion 107
5.4. Physiological and neurological perspectives 112
5.5. Psychological theory comparison within the field of AI design 114
5.6. Verbal and textual indicators 117
5.7. Use of language and sentiment analysis 117
5.8. Psycholinguistic features, emotional valence 117
5.9. Prosody and SER 117
5.10. Culture and background information 118
5.11. Individual and cultural variations 118Contents ix
5.12. Variations in people and populations 119
5.13. Emotional norms and cultural influences 119
5.14. Difficulties in universal emotion recognition 120
5.15. Bridging psychology and AI in emotion detection 121
5.16. How AI models interpret human emotions 121
5.17. Emotion recognition using ML methods 122
5.18. Sentiment analysis using NLP 122
5.19. Facial and gesture analysis – computer vision 123
5.20. Psychological principles in AI design 124
5.21. Case studies and applications 128
5.22. Human–computer interaction 131
5.23. Emerging trends, open questions and ethical considerations in emotion AI 136
5.24. The ethical development of AI in emo-sensitive apps 139
5.25. Conclusion 140
5.26. References 142

Chapter 6. Machine Learning Approaches for Sentiment Analysis 155
T.C. Swetha PRIYA and A. Kanaka DURGA

6.1. Introduction 156
6.2. Fundamentals of sentiment analysis 157
6.3. Introduction to machine learning 160
6.4. Sentiment analysis using machine learning 161
6.5. Comparative analysis of traditional machine learning, deep learning and LLM approaches 173
6.6. Applications of sentiment analysis 174
6.7. Conclusion 175
6.8. References 176

Chapter 7. Natural Language Processing for Sentiment Analysis 179
P.R. ANISHA, Umaima Qader MOHIUDDIN and Hajira FAROOQUI

7.1. Introduction 180
7.2. Background and related work 182
7.3. LLMs for sentiment analysis 183
7.4. Applications in financial and economic domains 186
7.5. Case study: Financial and economics with LLMs 188
7.6. Evaluation metrics 195
7.7. Challenges and limitations 196
7.8. Conclusion 196
7.9. Results 197
7.10. Future scope 200
7.11. References 200

Chapter 8. Decoding Human Affect: A Structural Equation Modeling Approach to Deep Learning-Based Emotional and Sentiment Analysis 203
Remmiya Rajan P., Kolapo IGE and Dineshan E.

8.1. Introduction 203
8.2. Study rationale 205
8.3. Study objectives 207
8.4. Study hypotheses 207
8.5. Review of related literature 208
8.6. Study methodology 212
8.7. Analysis and interpretation 215
8.8. Study findings 227
8.9. Conclusion 228
8.10. References 228

Chapter 9. AI in Analyzing Emotions on Social Media Platforms: A Paradigm Shift 231
Md. Saddam HOSSAIN, Farhana YEASMIN and Md. Shajahan KABIR

9.1. Introduction 231
9.2. Theoretical framework: affective computing and emotion recognition 234
9.3. Proposed conceptual framework 236
9.4. Characteristics and challenges of social media data 237
9.5. AI methodologies for emotion detection on social media 239
9.6. Applications and case studies 240
9.7. Ethical considerations in AI-based sentiment analysis 243
9.8. Conclusion and future directions 244
9.9. References 248

Chapter 10. Human Emotion Recognition Using Speech Signals 253
Geetanjali SRIVASTAVA, Girish SURSAKAR, Akash VISHWAKARMA and Priyanka JAIN

10.1. Introduction 254
10.2. Related work 255
10.3. Methodology 258
10.4. Experimental results and discussions 269
10.5. Forensic applications and behavioral surveillance 271
10.6. Conclusion 273
10.7. References 273

Chapter 11. Transforming Employee Relations: The Role of Speech Emotion Recognition in Modern HR Practices 281
Revati Ramrao RAUTRAO

11.1. Introduction 282
11.2. Background 294
11.3. Focus of the chapter 295
11.4. Conclusion 298
11.5. References 299

Chapter 12. Fuzzy and Neutrosophic Logic-Based Models for Handling Uncertainty in Emotion Detection 303
Ajoy Kanti DAS, Nandini GUPTA, Suman PATRA and Takaaki FUJITA

12.1. Introduction 303
12.2. Basics of fuzzy and neutrosophic logic 309
12.3. Understanding uncertainty in emotions 311
12.4. Fuzzy logic-based emotion detection models 314
12.5. Neutrosophic logic-based emotion detection models 318
12.6. Comparison with classical methods. 321
12.7. Applications in real life 323
12.8. Challenges and integration with modern AI 326
12.9. Conclusion 329
12.10. References 331

Chapter 13. The Evolution of Emotion Research: From Darwin’s Theories to AI 337
Sirine Hadjer ZAABTA, Omar MATARI and Omar SEBBAGH

13.1. Introduction 338
13.2. Evolutionary foundations of emotion research 339
13.3. From marginalization to cognitive integration 346
13.4. The rise of emotional neuroscience 348
13.5. Toward affective computing and AI integration 352
13.6. HGEAIF: bridging psychological theory and modern computation 357
13.7. Ethical and conceptual challenges 360
13.8. Conclusion 362
13.9. References 363

Chapter 14. Real-Time Face Emotion Detection using Mobilenetv1 and Speech Emotion Recognition using Multimodal Analysis 385
Budhaditya BHATTACHARYYA

14.1. Introduction 385
14.2. Technical specifications 386
14.3. Speech emotion recognition 387
14.4. Data preprocessing for audio analysis 390
14.5. Simulation results 392
14.6. Conclusion 403
14.7. References 404

List of Authors 407
Index 411