EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912383675400192 |
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| author | Lu, Xingyuan Liu, Yuxi Zhang, Dongyu Wu, Zhiyao Ren, Jing Xia, Feng |
| author_facet | Lu, Xingyuan Liu, Yuxi Zhang, Dongyu Wu, Zhiyao Ren, Jing Xia, Feng |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_13483 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors Lu, Xingyuan Liu, Yuxi Zhang, Dongyu Wu, Zhiyao Ren, Jing Xia, Feng Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.13483 |