EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
This addresses the scarcity of multimodal metaphorical emotion datasets, particularly for Chinese, which is important for researchers in natural language processing and emotional AI.
The authors tackled the problem of emotion classification in multimodal metaphors by creating EmoMeta, a Chinese dataset of 5,000 text-image pairs from advertisements annotated for metaphor occurrence, domain relations, and 10 fine-grained emotions.
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.