# Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English

arXiv: https://arxiv.org/abs/2504.17974 · HTML: https://mlsanity.com/paper/2504.17974/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis](https://mlsanity.com/paper/2205.01996/) (2022)
- [Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue](https://mlsanity.com/paper/1709.05404/) (2017)
- [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1610.08815/) (2016)
- [Hope Speech Detection: A Computational Analysis of the Voice of Peace](https://mlsanity.com/paper/1909.12940/) (2019)
- [PolyHope: Two-Level Hope Speech Detection from Tweets](https://mlsanity.com/paper/2210.14136/) (2022)
- [Debiasing Multimodal Sarcasm Detection with Contrastive Learning](https://mlsanity.com/paper/2312.10493/) (2023)
- [Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media](https://mlsanity.com/paper/2005.11424/) (2020)
- [Cross-lingual transfer of multilingual models on low resource African Languages](https://mlsanity.com/paper/2409.10965/) (2024)

## Built on by (most cited first, 2 of 2)

- [Classification of Hope in Textual Data using Transformer-Based Models](https://mlsanity.com/paper/2511.12874/) (2025)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)

## Abstract (arXiv)

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.