Book

Description

How can a computer understand whether a sentence expresses anger, fear, joy, love, sadness, or surprise? Emotion Detection Using a Transformer Model with Deep Learning explores this fascinating challenge through Natural Language Processing (NLP) and BERT-based deep learning. The book presents a practical six-class emotion classification framework using a fine-tuned BERT Transformer model. It explains the complete process, from text preprocessing and tokenization to contextual embeddings, self-attention, model training, and performance evaluation. The core study reports 93.0% test accuracy and a macro F1-score of 0.8977 on 6,000 held-out samples. It also examines classification errors, class imbalance, methodological limitations, and opportunities for future improvement. Beyond the core experiment, the book discusses four recent research studies and provides practical explanations, illustrative examples, imagined deployment scenarios, a checklist, and practice questions. Designed for students, researchers, academicians, and practitioners, this book offers an accessible introduction to transformer-based emotion detection and its applications in sentiment analysis, customer experience, human-computer interaction, and responsible AI. Explore how deep learning can move beyond identifying words to recognising the emotions expressed through language.