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

TL;DR

This paper introduces EmoMeta, a comprehensive multimodal Chinese dataset of 5,000 metaphorical advertisements annotated for emotions, aiming to advance fine-grained emotion classification in multimodal metaphors, especially in Chinese language contexts.

Contribution

The creation of a large, annotated multimodal Chinese metaphor dataset for emotion classification, addressing the lack of resources and focusing on fine-grained emotions across modalities.

Findings

Dataset includes 5,000 text-image pairs.

Annotations cover metaphor occurrence, domain relations, and 10 emotion categories.

Dataset is publicly available for research use.

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.