构建多语言希望话语数据集，区分乐观、幻想与讽刺等细微情感
Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English
- 构建3万+条中英文微博标注数据，细分为四类希望情绪
- 微调Transformer模型在识别讽刺和细微差异上优于大模型
- 为跨语言情感分析提供高精度语境敏感基准
希望是一种复杂且研究不足的情感状态，在教育、心理健康和社会互动中具有重要意义。与基本情绪不同，希望表现为从现实乐观到过度幻想或讽刺等多种微妙形式，给自然语言处理系统带来准确检测的挑战。本研究提出PolyHope V2，一个包含超过30,000条英语和西班牙语微博的多语言、细粒度希望话语数据集，区分四类子类型：泛化性希望、现实性希望、非现实性希望和讽刺性希望，并通过显式标注讽刺实例增强现有数据集。我们对多种预训练Transformer模型进行基准测试，并与GPT-4和Llama 3等大语言模型在零样本和少样本设置下进行对比。结果表明，微调后的Transformer模型在区分细微希望类别及讽刺表达方面表现更优。通过定性分析和混淆矩阵，揭示了在相近希望子类型间区分时存在的系统性挑战。该数据集与实验结果为未来需要更高语义与上下文敏感性的跨语言情感识别任务提供了坚实基础。
原文摘要 · Abstract (English)
Hope is a complex and underexplored emotional state that plays a significant role in education, mental health, and social interaction. Unlike basic emotions, hope manifests in nuanced forms ranging from grounded optimism to exaggerated wishfulness or sarcasm, making it difficult for Natural Language Processing systems to detect accurately. This study introduces PolyHope V2, a multilingual, fine-grained hope speech dataset comprising over 30,000 annotated tweets in English and Spanish. This resource distinguishes between four hope subtypes Generalized, Realistic, Unrealistic, and Sarcastic and enhances existing datasets by explicitly labeling sarcastic instances. We benchmark multiple pretrained transformer models and compare them with large language models (LLMs) such as GPT 4 and Llama 3 under zero-shot and few-shot regimes. Our findings show that fine-tuned transformers outperform prompt-based LLMs, especially in distinguishing nuanced hope categories and sarcasm. Through qualitative analysis and confusion matrices, we highlight systematic challenges in separating closely related hope subtypes. The dataset and results provide a robust foundation for future emotion recognition tasks that demand greater semantic and contextual sensitivity across languages.
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