  Video content is rich in semantics and has the ability to evoke various
emotions in viewers. In recent years, with the rapid development of affective
computing and the explosive growth of visual data, affective video content
analysis (AVCA) as an essential branch of affective computing has become a
widely researched topic. In this study, we comprehensively review the
development of AVCA over the past decade, particularly focusing on the most
advanced methods adopted to address the three major challenges of video feature
extraction, expression subjectivity, and multimodal feature fusion. We first
introduce the widely used emotion representation models in AVCA and describe
commonly used datasets. We summarize and compare representative methods in the
following aspects: (1) unimodal AVCA models, including facial expression
recognition and posture emotion recognition; (2) multimodal AVCA models,
including feature fusion, decision fusion, and attention-based multimodal
models; (3) model performance evaluation standards. Finally, we discuss future
challenges and promising research directions, such as emotion recognition and
public opinion analysis, human-computer interaction, and emotional
intelligence.
