Mapping Gestures Based on Text Emotion Classification for a Virtual Chatbot for Early Marriage Consultation in Lombok Using RoBERTa Model - Ramadhan, Adam Zahran; Wijaya, Rifki; Shaufiah, Shaufiah (Jurnal Teknik Informatika (Jutif), 2025)
To address the persistent issue of early marriage among Indonesian adolescents, this study proposes a virtual counseling chatbot that classifies emotional cues in text using a fine-tuned IndoRoBERTa model. The emotion classification framework is designed to support counseling-based prevention efforts by moving beyond basic sentiment analysis and adopting five functional emotional categories such as ‘Enthusiastic’, ‘Gentle’, ‘Analytical’, ‘Inspirational’, and ‘Cautionary’ to align with psychological counseling styles. Built on fine-tuned IndoRoBERTa architecture, the model was trained in two phases: first with 2,500 manually validated samples yielding 92.8% accuracy, and then with 12,500 auto-labeled entries, resulting in 91.3% accuracy. Performance was assessed using accuracy, precision, recall, and F1-score. A gesture mapping layer was also integrated to enhance empathetic response generation. Each emotion label was paired with a predefined body gesture, grounded in counseling theory, to support future development of multimodal virtual agents capable of expressing emotions both textually and physically. The novelty lies in combining context-aware emotion classification with gesture mapping, enabling future development of expressive, culturally relevant, and empathetic virtual chatbot agents.