mlsanityData to 7 Oct 2026

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

arXiv 2305.07665 ↗First posted 8 May 2023cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 146 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

22 of 146 references.

References · 22

  1. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
  2. 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 479
  3. 2013Challenges in Representation Learning: A report on three machine learning contestscited by 301
  4. 2018Deep Facial Expression Recognition: A Surveycited by 269
  5. 2020Suppressing Uncertainties for Large-Scale Facial Expression Recognitioncited by 157
  6. 2020DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wildcited by 128
  7. 2019Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advancescited by 118
  8. 2017Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasmcited by 117
  9. 2022A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advancescited by 59
  10. 2016From Facial Expression Recognition to Interpersonal Relation Predictioncited by 53
  11. 2016Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applicationscited by 45
  12. 2019Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysiscited by 39
10 more
  1. 2021SpanEmo: Casting Multi-label Emotion Classification as Span-predictioncited by 37
  2. 2016Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysiscited by 35
  3. 2019A Deeper Look at Facial Expression Dataset Biascited by 25
  4. 2019Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classificationcited by 22
  5. 2019Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Surveycited by 17
  6. 2020TSception: A Deep Learning Framework for Emotion Detection Using EEGcited by 14
  7. 2021Virtual Reality for Emotion Elicitation -- A Reviewcited by 11
  8. 2016Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machinescited by 10
  9. 2018A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metricscited by 9
  10. 2021Magnifying Subtle Facial Motions for Effective 4D Expression Recognitioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.

  1. 2024AutoSurvey: Large Language Models Can Automatically Write Surveyscited by 116
  2. 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspectivecited by 24
  3. 2025When LLMs Team Up: The Emergence of Collaborative Affective Computingcited by 2
  4. 2026EmoScene: A Dual-space Dataset for Controllable Affective Image Generationcited by 1
  5. 2024TinyEmo: Scaling down Emotional Reasoning via Metric Projectioncited by 0
  6. 2025Continuous Adversarial Text Representation Learning for Affective Recognitioncited by 0
  7. 2026A Baseline Multimodal Approach to Emotion Recognition in Conversationscited by 0