ManzaiSet: A Multimodal Dataset of Viewer Responses to Japanese Manzai Comedy
This enables culturally aware emotion AI development and personalized entertainment systems for non-Western contexts, though it is incremental as it applies existing methods to new data.
The researchers tackled the Western-centric bias in affective computing by creating ManzaiSet, a large multimodal dataset of viewer responses to Japanese manzai comedy, which revealed three distinct viewer types and a positive viewing order effect with a mean slope of 0.488.
We present ManzaiSet, the first large scale multimodal dataset of viewer responses to Japanese manzai comedy, capturing facial videos and audio from 241 participants watching up to 10 professional performances in randomized order (94.6 percent watched >= 8; analyses focus on n=228). This addresses the Western centric bias in affective computing. Three key findings emerge: (1) k means clustering identified three distinct viewer types: High and Stable Appreciators (72.8 percent, n=166), Low and Variable Decliners (13.2 percent, n=30), and Variable Improvers (14.0 percent, n=32), with heterogeneity of variance (Brown Forsythe p < 0.001); (2) individual level analysis revealed a positive viewing order effect (mean slope = 0.488, t(227) = 5.42, p < 0.001, permutation p < 0.001), contradicting fatigue hypotheses; (3) automated humor classification (77 instances, 131 labels) plus viewer level response modeling found no type wise differences after FDR correction. The dataset enables culturally aware emotion AI development and personalized entertainment systems tailored to non Western contexts.