Sentiment Analysis in NLP
Autor Shanliang Yangen Limba Engleză Hardback – 8 oct 2026
As sentiment analysis evolves from simple lexicon matching to sophisticated multimodal deep learning, practitioners need authoritative guidance spanning the field's entire trajectory. Sentiment Analysis in NLP delivers this breadth, covering text-based, aspect-based, multimodal, and implicit sentiment analysis, integrating text, audio, and visual data processing while addressing both theoretical foundations and real-world implementations.
This book examines neural network architectures including CNNs and RNNs for text analysis, transformer models like BERT, and Graph Attention Networks. Dedicated chapters cover attention mechanisms and generative AI for synthetic data generation. Practical applications span product development, social media monitoring, and public health surveillance. Python code, datasets, and a solutions manual support hands-on learning.
Readers will also find:
- Multimodal sentiment analysis techniques integrating text, speech, and image data to interpret emotional content across diverse communication formats
- Transformer-based models and attention mechanisms including BERT and GPT architectures that have transformed state-of-the-art sentiment classification performance
- Real-time context-aware sentiment analysis systems designed for continuous monitoring applications in social media and business intelligence environments
- Ethical considerations addressing data privacy, algorithmic bias, and transparency challenges that practitioners face when deploying sentiment analysis systems
- Case studies demonstrating sentiment analysis applications across customer feedback analysis, public safety monitoring, and healthcare decision support contexts
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Specificații
ISBN-13: 9781394349111
ISBN-10: 1394349114
Pagini: 320
Greutate: 0.6 kg
Editura: John Wiley & Sons, Inc.
ISBN-10: 1394349114
Pagini: 320
Greutate: 0.6 kg
Editura: John Wiley & Sons, Inc.
Notă biografică
SHANLIANG YANG is a Lecturer and Master's Supervisor in the Department of Artificial Intelligence at the School of Computer Science and Technology, Shandong University of Technology. His research focuses on sentiment analysis, large language models, generative AI, and public health applications, bridging theoretical NLP advances with practical implementations across multiple domains.
Cuprins
Acknowledgments xv
About the Author xvii
About the Companion Website xix
1 Introduction 1
1.1 Overview of Sentiment Analysis 1
1.2 Fundamentals of Sentiment Analysis 14
1.3 Historical Evolution of Sentiment Analysis 20
1.4 Book Structure 26
2 Basic Concepts and Techniques in Sentiment Analysis 33
2.1 Basic Concepts of Sentiment Analysis 33
2.2 Data Sources for Sentiment Analysis 36
2.3 Core Techniques in Text Sentiment Analysis 40
2.4 Speech Sentiment Analysis 49
2.5 Visual Sentiment Analysis 54
2.6 Evaluation and Selection of Sentiment Analysis 59
2.7 Summary 62
3 Text-based Sentiment Analysis 67
3.1 Introduction to Text-based Sentiment Analysis 68
3.2 Lexical-based Approaches for Text Sentiment Analysis 71
3.3 ML Approaches for Text Sentiment Analysis 75
3.4 Deep Learning Approaches for Text Sentiment Analysis 80
3.5 Advanced Topics in Text-based Sentiment Analysis 86
3.6 Case Study 92
3.7 Summary and Future Directions in Text-based Sentiment Analysis 95
4 Implicit Sentiment Analysis 99
4.1 Defining Implicit Sentiment 100
4.2 The Knowledge-augmented Paradigm: Injecting Commonsense for Implicit Sentiment Reasoning 105
4.3 The Contextual Representation Learning Paradigm: Learning Implicit Sentiment Understanding from Context 113
4.4 The Prompt-based Reasoning Paradigm: Guiding Large Models for Implicit Sentiment Interpretation 120
4.5 Research Frontiers and Open Challenges in Implicit Sentiment Analysis 125
4.6 Case Study: A Deep Dive into Reasoning-based Implicit Sentiment Interpretation with LLMs 128
4.7 Conclusion 133
5 Multimodal Sentiment Analysis 137
5.1 Introduction to MSA 138
5.2 Representation and Alignment of Multimodal Data 141
5.3 Multimodal Fusion Strategies: From Simple Concatenation to Intelligent Interaction 147
5.4 Deep Learning Architectures for MSA 154
5.5 Case Study: Analyzing Sentiment in YouTube Opinion Videos 162
5.6 Chapter Summary and Outlook 172
6 Application of Attention Mechanisms in Sentiment Analysis 175
6.1 Introduction 175
6.2 Basic Principles and Evolution of Attention Mechanisms 178
6.3 Transformer Architecture: A Revolutionary Application of Attention Mechanisms 182
6.4 In-depth Application of Attention Mechanisms in Various Sentiment Analysis Tasks 186
6.5 Advanced Attention Variants and GATs 192
6.6 Practical Cases and Code Implementation 196
6.7 Challenges, Limitations, and Future Outlook 200
6.8 Chapter Summary 202
7 Advanced Technologies and Future Trends 205
7.1 The Role of LLMs: From BERT to GPT 206
7.2 Application of Generative AI in Sentiment Analysis 214
7.3 Cross-language and Cross-cultural Sentiment Analysis 217
7.4 Future Development Direction 226
8 Practical Applications of Sentiment Analysis 235
8.1 Business Intelligence: From VoC to Strategic Advantage 236
8.2 Public Safety and Governance: Sensing the Pulse of Society 242
8.3 Public Health: A New Window into Community Well-being 248
8.4 Technical Deep Dive: Unique Challenges and Strategies in Social Media Sentiment Analysis 255
8.5 Practitioner's Guide: Tools, Platforms, and Selection Methods 258
9 Conclusion and Outlook 267
9.1 A Synthesis of the Field: Evolutionary Trajectory and Core Insights 268
9.2 The Ethical Compass: Navigating Responsibly in the Age of Affective Computing 272
9.3 Future Research Directions 277
9.4 The Responsibility and Promise of Affective Intelligence 281
References 282
Index 285
About the Author xvii
About the Companion Website xix
1 Introduction 1
1.1 Overview of Sentiment Analysis 1
1.2 Fundamentals of Sentiment Analysis 14
1.3 Historical Evolution of Sentiment Analysis 20
1.4 Book Structure 26
2 Basic Concepts and Techniques in Sentiment Analysis 33
2.1 Basic Concepts of Sentiment Analysis 33
2.2 Data Sources for Sentiment Analysis 36
2.3 Core Techniques in Text Sentiment Analysis 40
2.4 Speech Sentiment Analysis 49
2.5 Visual Sentiment Analysis 54
2.6 Evaluation and Selection of Sentiment Analysis 59
2.7 Summary 62
3 Text-based Sentiment Analysis 67
3.1 Introduction to Text-based Sentiment Analysis 68
3.2 Lexical-based Approaches for Text Sentiment Analysis 71
3.3 ML Approaches for Text Sentiment Analysis 75
3.4 Deep Learning Approaches for Text Sentiment Analysis 80
3.5 Advanced Topics in Text-based Sentiment Analysis 86
3.6 Case Study 92
3.7 Summary and Future Directions in Text-based Sentiment Analysis 95
4 Implicit Sentiment Analysis 99
4.1 Defining Implicit Sentiment 100
4.2 The Knowledge-augmented Paradigm: Injecting Commonsense for Implicit Sentiment Reasoning 105
4.3 The Contextual Representation Learning Paradigm: Learning Implicit Sentiment Understanding from Context 113
4.4 The Prompt-based Reasoning Paradigm: Guiding Large Models for Implicit Sentiment Interpretation 120
4.5 Research Frontiers and Open Challenges in Implicit Sentiment Analysis 125
4.6 Case Study: A Deep Dive into Reasoning-based Implicit Sentiment Interpretation with LLMs 128
4.7 Conclusion 133
5 Multimodal Sentiment Analysis 137
5.1 Introduction to MSA 138
5.2 Representation and Alignment of Multimodal Data 141
5.3 Multimodal Fusion Strategies: From Simple Concatenation to Intelligent Interaction 147
5.4 Deep Learning Architectures for MSA 154
5.5 Case Study: Analyzing Sentiment in YouTube Opinion Videos 162
5.6 Chapter Summary and Outlook 172
6 Application of Attention Mechanisms in Sentiment Analysis 175
6.1 Introduction 175
6.2 Basic Principles and Evolution of Attention Mechanisms 178
6.3 Transformer Architecture: A Revolutionary Application of Attention Mechanisms 182
6.4 In-depth Application of Attention Mechanisms in Various Sentiment Analysis Tasks 186
6.5 Advanced Attention Variants and GATs 192
6.6 Practical Cases and Code Implementation 196
6.7 Challenges, Limitations, and Future Outlook 200
6.8 Chapter Summary 202
7 Advanced Technologies and Future Trends 205
7.1 The Role of LLMs: From BERT to GPT 206
7.2 Application of Generative AI in Sentiment Analysis 214
7.3 Cross-language and Cross-cultural Sentiment Analysis 217
7.4 Future Development Direction 226
8 Practical Applications of Sentiment Analysis 235
8.1 Business Intelligence: From VoC to Strategic Advantage 236
8.2 Public Safety and Governance: Sensing the Pulse of Society 242
8.3 Public Health: A New Window into Community Well-being 248
8.4 Technical Deep Dive: Unique Challenges and Strategies in Social Media Sentiment Analysis 255
8.5 Practitioner's Guide: Tools, Platforms, and Selection Methods 258
9 Conclusion and Outlook 267
9.1 A Synthesis of the Field: Evolutionary Trajectory and Core Insights 268
9.2 The Ethical Compass: Navigating Responsibly in the Age of Affective Computing 272
9.3 Future Research Directions 277
9.4 The Responsibility and Promise of Affective Intelligence 281
References 282
Index 285