# Historical Ink: Exploring Large Language Models for Irony Detection in 19th-Century Spanish

arXiv: https://arxiv.org/abs/2503.22585 · HTML: https://mlsanity.com/paper/2503.22585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Augmenting emotion features in irony detection with Large language modeling](https://mlsanity.com/paper/2404.12291/) (2024)
- [Make Satire Boring Again: Reducing Stylistic Bias of Satirical Corpus by Utilizing Generative LLMs](https://mlsanity.com/paper/2412.09247/) (2024)
- [Historical Ink: 19th Century Latin American Spanish Newspaper Corpus with LLM OCR Correction](https://mlsanity.com/paper/2407.12838/) (2024)
- [Historical Ink: Semantic Shift Detection for 19th Century Spanish](https://mlsanity.com/paper/2407.12852/) (2024)

## Built on by (most cited first, 1 of 1)

- [Irony Detection in Urdu Text: A Comparative Study Using Machine Learning Models and Large Language Models](https://mlsanity.com/paper/2510.22356/) (2025)

## Abstract (arXiv)

This study explores the use of large language models (LLMs) to enhance datasets and improve irony detection in 19th-century Latin American newspapers. Two strategies were employed to evaluate the efficacy of BERT and GPT-4o models in capturing the subtle nuances nature of irony, through both multi-class and binary classification tasks. First, we implemented dataset enhancements focused on enriching emotional and contextual cues; however, these showed limited impact on historical language analysis. The second strategy, a semi-automated annotation process, effectively addressed class imbalance and augmented the dataset with high-quality annotations. Despite the challenges posed by the complexity of irony, this work contributes to the advancement of sentiment analysis through two key contributions: introducing a new historical Spanish dataset tagged for sentiment analysis and irony detection, and proposing a semi-automated annotation methodology where human expertise is crucial for refining LLMs results, enriched by incorporating historical and cultural contexts as core features.