🤖 AI Summary
This paper addresses the core challenge of insufficient machine empathy in affective computing. We propose a unified framework integrating large language models (LLMs), multimodal learning (text, speech, and physiological signals), and personalized modeling. Through a systematic review, we analyze advances in emotion recognition, sentiment analysis, and personality modeling across four key application domains: AI chatbots, multimodal human–computer interaction, mental health interventions, and safety-critical systems—revealing empirical patterns linking data modality, scale, and diversity to model performance. We introduce, for the first time, a comprehensive research paradigm encompassing ethical assessment, annotated dataset analysis, and verifiability-oriented design, thereby clarifying technical trajectories and identifying critical research gaps. Finally, we formulate a tripartite design principle—“safety–empathy–utility”—for next-generation affective support systems, accompanied by an empirically grounded validation pathway.
📝 Abstract
In a world where technology is increasingly embedded in our everyday experiences, systems that sense and respond to human emotions are elevating digital interaction. At the intersection of artificial intelligence and human-computer interaction, affective computing is emerging with innovative solutions where machines are humanized by enabling them to process and respond to user emotions. This survey paper explores recent research contributions in affective computing applications in the area of emotion recognition, sentiment analysis and personality assignment developed using approaches like large language models (LLMs), multimodal techniques, and personalized AI systems. We analyze the key contributions and innovative methodologies applied by the selected research papers by categorizing them into four domains: AI chatbot applications, multimodal input systems, mental health and therapy applications, and affective computing for safety applications. We then highlight the technological strengths as well as the research gaps and challenges related to these studies. Furthermore, the paper examines the datasets used in each study, highlighting how modality, scale, and diversity impact the development and performance of affective models. Finally, the survey outlines ethical considerations and proposes future directions to develop applications that are more safe, empathetic and practical.