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Table of contents
Stance Detection in Natural Language Processing
A graduate-level path from linguistic foundations and neural modeling to robust, deployable stance detection systems
Read each section in order. Every title can be opened as a TheoryTrace document.
- Cover1
- Copyright2
- How to read this book3
- Introduction4
- Chapter 1: The Problem of Stance Detection5
- Chapter 2: Linguistic and Pragmatic Foundations6
- Chapter 3: Mathematical Foundations for NLP Models7
- Chapter 4: Data, Annotation, and Task Design8
- Chapter 5: Text Preprocessing and Classical Feature Engineering9
- Chapter 6: Classical Machine Learning Baselines10
- Chapter 7: Distributed Representations and Neural Sequence Models11
- Chapter 8: Transformer Architectures for Stance Detection12
- Chapter 9: Target-Aware and Context-Aware Modeling13
- Chapter 10: Evaluation Methodology and Experimental Design14
- Chapter 11: Domain Adaptation and Cross-Target Generalization15
- Chapter 12: Data Scarcity, Weak Supervision, and Semi-Supervised Learning16
- Chapter 13: Prompting and Large Language Models17
- Chapter 14: Argumentation, Evidence, and Reasoning18
- Chapter 15: Conversation, Social Networks, and User-Level Stance19
- Chapter 16: Multilingual and Cross-Lingual Stance Detection20
- Chapter 17: Robustness, Bias, Fairness, and Ethics21
- Chapter 18: Interpretability and Model Diagnostics22
- Chapter 19: Building a Research-Grade Stance Detection System23
- Chapter 20: Deployment, Monitoring, and Research Frontiers24
- Conclusion25