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arXiv · 2505.13483

EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors

Xingyuan Lu·Yuxi Liu·Dongyu Zhang·Zhiyao Wu·Jing Ren·Feng Xia
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Abstract

Metaphors play a pivotal role in expressing emotions, making them crucial for emotional intelligence. The advent of multimodal data and widespread communication has led to a proliferation of multimodal metaphors, amplifying the complexity of emotion classification compared to single-mode scenarios. However, the scarcity of research on constructing multimodal metaphorical fine-grained emotion datasets hampers progress in this domain. Moreover, existing studies predominantly focus on English, overlooking potential variations in emotional nuances across languages. To address these gaps, we introduce a multimodal dataset in Chinese comprising 5,000 text-image pairs of metaphorical advertisements. Each entry is meticulously annotated for metaphor occurrence, domain relations and fine-grained emotion classification encompassing joy, love, trust, fear, sadness, disgust, anger, surprise, anticipation, and neutral. Our dataset is publicly accessible (https://github.com/DUTIR-YSQ/EmoMeta), facilitating further advancements in this burgeoning field.

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Xingyuan Lu, Yuxi Liu, Dongyu Zhang, Zhiyao Wu, Jing Ren, Feng Xia. 2025-05-12. EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors. https://doi.org/10.1145/3701716.3735080

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