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arXiv Computation and Language By Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat

MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions

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MultiHuSE is a multimodal dataset featuring 2,407 high‑definition videos of 50 diverse actors delivering 1,463 text samples in four psychological humour styles—affiliative, aggressive, self‑enhancing, and self‑deprecating—plus neutral content. Each text is performed by multiple actors, allowing analysis of expressive diversity, and a subset includes emotion annotations. Baseline experiments show that multimodal fusion improves humour style classification accuracy over unimodal approaches, especially for affiliative humour.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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