Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English
This work addresses the challenge of fine-grained emotion recognition for NLP researchers, though it is incremental as it builds on existing datasets and methods.
The study tackled the problem of detecting nuanced hope speech subtypes in English and Spanish tweets, introducing PolyHope V2 with over 30,000 annotated examples and finding that fine-tuned transformers outperform large language models like GPT-4 and Llama 3 in distinguishing categories such as sarcasm.
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.