Sentiment Analysis in NLP : Techniques, Applications, and Future Directions

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Author(s):	Yang Yang, Shanliang
ISBN No.:	9781394349111
Pages:	320
Year:	202611
Format:	Trade Cloth (Hard Cover)
Price:	$ 203.83
Dispatch delay:	Dispatched between 7 to 15 days
Status:	Available (Forthcoming)

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TABLE OF CONTENTS CHAPTER 1 : I ntroduction Chapter 1: Introduction 1.1 Overview of Sentiment Analysis 1.1.1 Definition and Scope of Sentiment Analysis 1.1.2 The Growing Importance of Sentiment Analysis 1.1.3 Applications Across Various Domains 1.

1.4 Research Significance, Challenges, and Future Directions 1.2 Fundamentals of Sentiment Analysis 1.2.1 Core Principles and Tasks 1.2.2 Typical Methods in Sentiment Analysis 1.3 Historical Evolution of Sentiment Analysis 1.

3.1 Early Approaches: Rule-Based and Lexicon-Based 1.3.2 The Rise of Machine Learning and Statistical Methods 1.3.3 Deep Learning and Modern Methods 1.3.4 Current Challenges and Limitations 1.

4 Book Structure 1.4.1 Overview of Chapter Structure 1.4.2 Integration of Chapters References Chapter 2: Basic Concepts and Techniques in Sentiment Analysis 2.1 Basic Concepts of Sentiment Analysis 2.1.1 Definition of Sentiment 2.

1.2 Types of Sentiment 2.1.3 Sentiment vs. Opinion 2.2 Data Sources for Sentiment Analysis 2.2.1 Social Media Platforms 2.

2.2 Customer Reviews 2.2.3 News Articles 2.2.4 Forums and Blogs 2.2.5 Customer Service Interactions 2.

3 Core Techniques in Text Sentiment Analysis 2.3.1 Lexical-based Approaches 2.3.2 Machine Learning Methods 2.3.3 Deep Learning Techniques 2.4 Speech Sentiment Analysis 2.

4.1 Overview of Speech Sentiment Analysis 2.4.2 Techniques in Speech Sentiment Analysis 2.4.3 Applications of Speech Sentiment Analysis 2.5 Visual Sentiment Analysis 2.5.

1 Overview of Visual Sentiment Analysis 2.5.2 Techniques in Visual Sentiment Analysis 2.5.3 Applications of Visual Sentiment Analysis 2.6 Evaluation and Selection of Sentiment Analysis 2.6.1 Evaluation Metrics for Sentiment Analysis 2.

6.2 Considerations for Selecting and Tailoring Sentiment Analysis Approaches 2.7 Summary References Chapter 3: Text-Based Sentiment Analysis 3.1 Introduction to Text-Based Sentiment Analysis 3.1.1 The Primacy and Pervasiveness of Textual Data for Sentiment Analysis 3.1.2 Defining Sentiment in the Textual Modality 3.

1.3 Key Challenges Inherent in Textual Sentiment Analysis 3.2 Lexical-Based Approaches for Text Sentiment Analysis 3.2.1 Sentiment Lexicon Construction and Refinement 3.2.2 Advanced Rule-Based Systems for Text Sentiment 3.2.

3 Advantages and Limitations for Textual Lexical Methods 3.3 Machine Learning Approaches for Text Sentiment Analysis 3.3.1 Text Preprocessing and Feature Engineering for Text Sentiment 3.3.2 Application of Traditional ML Algorithms to Text Sentiment 3.3.3 Model Training, Hyperparameter Tuning, and Evaluation Strategies for Text 3.

4 Deep Learning Approaches for Text Sentiment Analysis 3.4.1 Word Embedding as Input Features 3.4.2 Neural Network Architectures for Text Sentiment 3.4.3 Transformer-Based Models and Pretrained Language Models 3.4.

4 Large Language Models (LLMs) for Text Sentiment Analysis 3.5 Advanced Topics in Text-Based Sentiment Analysis 3.5.1 Aspect-Based Sentiment Analysis (ABSA) for Text 3.5.2 Sarcasm, Irony, and Figurative Language Detection 3.5.3 Multilingual and Cross-Lingual Sentiment Analysis 3.

5.4 Explainable AI (XAI) for Text Sentiment Analysis 3.6 Case Study 3.6.1 Dataset 3.6.2 Data Preprocessing 3.6.

3 Modeling Approaches and Implementation 3.6.4 Comparative Results and Analysis 3.7 Summary and Future Directions in Text-Based Sentiment Analysis References Chapter 4: Implicit Sentiment Analysis 4.1 Defining Implicit Sentiment 4.1.1 From Explicit to Implicit 4.1.

2 The Two Foundational Problems 4.1.3 A Taxonomy of Implicit Sentiment Expressions 4.1.4 Chapter Roadmap 4.2 The Knowledge-Augmented Paradigm: Injecting Commonsense for Implicit Sentiment Reasoning 4.2.1 Aligning Symbolic Knowledge with Vector Spaces 4.

2.2 Feature Engineering with Lexicons and Knowledge Graphs for Implicit Cues 4.2.3 Knowledge Graph Embedding-Enhanced Neural Networks 4.2.4 Implicit Sentiment Reasoning with Graph Neural Network 4.3 The Contextual Representation Learning Paradigm: Learning Implicit Sentiment Understanding from Context 4.3.

1 The Distributional Hypothesis and the Primacy of Context in Implicit Analysis 4.3.2 Sequential Pattern Detection with Recurrent Neural Networks 4.3.3 Deep Contextual Understanding with Pre-trained Language Model 4.3.4 Contrastive Learning for Implicit Sentiment Differentiation 4.4 The Prompt-based Reasoning Paradigm: Guiding Large Models for Implicit Sentiment Interpretation 4.

4.1 Emergent Abilities and In-Context Learning in LLM 4.4.2 Zero-shot and Few-shot Implicit Sentiment Analysis via Prompt Engineering 4.4.3 Chain-of-Thought (CoT): Making the Reasoning Process for Implicit Sentiment Explicit 4.4.4 Advanced Prompting Strategies for Robust Reasoning 4.

5 Research Frontiers and Open Challenges in Implicit Sentiment Analysis 4.5.1 Implicit Aspect-Based Sentiment Analysis 4.5.2 Towards Causal Inference in Opinion Analysis 4.5.3 Multimodal Sarcasm Detection 4.6 Case Study: A Deep Dive into Reasoning-Based Implicit Sentiment Interpretation with LLMs 4.

6.1 Task Definition and Data Source 4.6.2 Chain-of-Thought Prompting 4.6.3 Critical Evaluation and Analysis 4.7 Conclusion 4.7.

1 Recapitulation of Challenges and Methodological Paradigms 4.7.2 From Sentiment Classification to Computational Empathy References Chapter 5: Multimodal Sentiment Analysis 5.1 Introduction to Multimodal Sentiment Analysis 5.1.1 The Cognitive Basis of Multimodal Communication 5.1.2 Bridging Implicit and Explicit Sentiment 5.

1.3 Core Concepts: Modality, Fusion, and Task Definition 5.2 Representation and Alignment of Multimodal Data 5.2.1 Unimodal Feature Engineering 5.2.2 Data Alignment and Synchronization 5.3 Multimodal Fusion Strategies: From Simple Concatenation to Intelligent Interaction 5.

3.1 Early Fusion and Late Fusion 5.3.2 Model-Level and Hybrid Fusion 5.3.3 A Core Challenge: Modeling Modality Incongruity 5.4 Deep Learning Architectures for Multimodal Sentiment Analysis 5.4.

1 Classic Architectures with CNNs and RNNs 5.4.2 Attention-Driven Fusion: The Rise of the Transformer Architecture 5.4.3 Benchmarks and Tools: Common Datasets and Evaluation Metrics 5.5 Case Study: Analyzing Sentiment in YouTube Opinion Videos 5.5.1 Problem Definition and Data Selection 5.

5.2 Feature Extraction and Alignment 5.5.3 Model Implementation 5.5.4 Result Analysis and Qualitative Insights 5.6 Chapter Summary and Outlook References Chapter 6: Application of Attention Mechanisms in Sentiment Analysis 6.1 Introduction 6.

1.1 Review of Limitations of Traditional Models 6.1.2 Intuitive Understanding of Attention Mechanisms 6.1.3 Chapter Structure and Learning Path 6.2 Basic Principles and Evolution of Attention Mechanisms 6.2.

1 General Framework of Attention Mechanisms 6.2.2 Two Classic Attention Models 6.2.3 Self-Attention Mechanism 6.3 Transformer Architecture: A Revolutionary Application of Attention Mechanisms 6.3.1 "Attention Is All You Need": The Birth of Transformer 6.

3.2 Multi-Head Self-Attention Mechanism 6.3.3 Overall Architecture of Transformer 6.4 In-Depth Application of Attention Mechanisms in Various Sentiment Analysis Tasks 6.4.1 Enhancement of Text Sentiment Analysis 6.4.

2 Breakthrough in Aspect-Based Sentiment Analysis (ABSA) 6.4.3 Cross-Modal Attention in Multimodal Sentiment Analysis 6.5 Advanced Attention Variants and Graph Attention Networks (GAT) 6.5.1 Born for Efficiency: Sparse Attention Mechanism 6.5.2 Graph Attention Networks (GAT) 6.

6 Practical Cases and Code Implementation 6.6.1 Case Study 6.6.2 Considerations in Practice 6.7 Challenges, Limitations, and Future Outlook 6.7.1 Current Challenges and Limitations 6.

7.2 Future Research Directions 6.8 Chapter Summary References Chapter 7: Advanced Technologies and Future Trends 7.1 The Role of Large Language Models (LLMs): From BERT to GPT 7.1.1 Introduction: Paradigm Shift from Traditional Models to Large Language Models 7.1.2 Transformer Architecture and Self-Attention Mechanism Essentials 7.

1.3 BERT and its variants: Applications of deep bidirectional contextual understanding 7.1.4 GPT Series and Generative Sentiment Analysis 7.1.5 Challenges and Limitations of LLMs 7.2 Application of Generative AI in sentiment analysis 7.2.

1 Synthetic Data Generation and Data Augmentation 7.2.2 Enhancing Model Robustness and Explainability 7.2.3 Potential Risks and Future Exploration 7.3 Cross-language and cross-cultural sentiment analysis 7.3.1 Definitions and Importance 7.

3.2 Key challenges: Language and cultural barriers 7.3.3 Mainstream technical methods 7.3.4 Future Research Directions 7.4 Future Development Direction 7.4.

1 Real-time sentiment analysis: Opportunities and technical requirements 7.4.2 Ethical considerations in sentiment analysis

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