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A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions

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arxiv 2305.07665 v1 pith:WGIT7FM3 submitted 2023-05-08 cs.AI

classification cs.AI
keywords affectivecomputingsurveyapplicationsapproachesdiscussfuturesignificance
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As the name suggests, affective computing aims to recognize human emotions, sentiments, and feelings. There is a wide range of fields that study affective computing, including languages, sociology, psychology, computer science, and physiology. However, no research has ever been done to determine how machine learning (ML) and mixed reality (XR) interact together. This paper discusses the significance of affective computing, as well as its ideas, conceptions, methods, and outcomes. By using approaches of ML and XR, we survey and discuss recent methodologies in affective computing. We survey the state-of-the-art approaches along with current affective data resources. Further, we discuss various applications where affective computing has a significant impact, which will aid future scholars in gaining a better understanding of its significance and practical relevance.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Affective-CARA: A Knowledge Graph Driven Framework for Culturally Adaptive Emotional Intelligence in HCI

    cs.HC 2025-06 reject novelty 4.0 of 10

    Affective-CARA integrates a hyperbolic culture emotion graph, a PPO-style reward optimizer, and a response mediator for culturally adaptive chatbot replies, but its headline metrics do not measure the claimed system behavior.

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