Sentiment and Emotion Modeling in Text-based Conversations utilizing ChatGPT - Mullangi, Pradeep; Dimmita, Nagajyothi; Supriya, M.; Murty, Patnala S. R. Chandra; Nirmala, Gera Vijaya; Palagan, C. Anna; Rao, Komati Thirupathi; Rajeswaran, N. (Engineering, Technology & Applied Science Research, 2025)
Emotional Intelligence (EI) constitutes a vital element of human communication, and its integration into text-based dialogues has gained great significance in the modern digital era. The present paper proposes an innovative method for modeling sentiment and emotion within text-based conversations using the ChatGPT language model. The advancements in sentiment and emotion recognition are centered on the role of EI in text-based conversational models. The study underscores the significance of diverse datasets, including Interactive Emotional Dyadic Motion Capture (IEMOCAP), MELD, EMORYNLP, and DAILYDIALOG, for training and evaluating emotion detection algorithms. IEMOCAP and MELD offer detailed emotional annotations, EMORYNLP emphasizes sensitive dialogue scenarios, and DAILYDIALOG encompasses a wide range of everyday interactions, providing distinct advantages for capturing emotional subtleties. The proficiency of different emotion categorization models, including ChatGPT and models with four levels of detail, is demonstrated through their capacity to understand and respond to emotions aptly. The crucial role of conversational AI with sophisticated EI in fostering empathy and context-sensitive interactions is emphasized.