构建首个中文隐喻情绪细粒度标注数据集，助力跨模态情感理解
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
- 采集5000个中英文图文隐喻广告对，标注隐喻与情绪关系
- 涵盖10类细粒度情绪（如喜悦、愤怒、惊讶等），支持精准分类
- 填补中文多模态隐喻情绪研究空白，适合情感计算与NLP研究者
隐喻在表达情感中起关键作用，是情感智能的重要组成部分。随着多模态数据的普及和传播方式的多样化，多模态隐喻大量涌现，使情绪分类比单模态场景更复杂。然而，针对多模态隐喻细粒度情绪数据集的构建研究匮乏，且现有工作主要集中在英文语境，忽视了语言间情感细微差异。为弥补这一空白，我们构建了一个中文多模态数据集EmoMeta，包含5000个文本-图像配对的隐喻广告样本。每条数据均经过精细标注，涵盖隐喻存在性、领域关系及10类细粒度情绪：喜悦、爱、信任、恐惧、悲伤、厌恶、愤怒、惊讶、期待和中性。该数据集已开源（https://github.com/DUTIR-YSQ/EmoMeta），可推动该新兴领域的持续发展。
原文摘要 · Abstract (English)
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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