---
license: cc-by-4.0
task_categories:
- text-classification
language:
- es
- en
tags:
- code-switching
- sentiment-analysis
- spanish
- english
- spanglish
- twitter
- hispanic-american
- low-resource
pretty_name: LINCE SA Refined — Cultural-Context Relabeling for Spanish-English Code-Switching Sentiment Analysis
size_categories:
- 1K<n<10K
source_datasets:
- lince-benchmark/lince
---

# LINCE SA Refined — Cultural-Context Relabeling for Spanish-English Code-Switching Sentiment Analysis

This repository releases **763 sentiment label refinements** for the Spanish-English code-switching subset (`sa_spaeng`) of the [LINCE benchmark](https://huggingface.co/datasets/lince-benchmark/lince) (Aguilar et al., 2020). Refinements were produced by a trilingual annotator (Spanish / English / Korean) drawing on Hispanic-American social media conventions, and validated through controlled mBERT experiments.

This work received an **Honorable Mention at the Korea Software Congress (KSC) 2025**.

## What this is

A **reproducible label-mapping** layered on top of LINCE SA. The original LINCE data is *not* redistributed here — only the refinements and a script that applies them.

- `label_mapping.json` — 763 entries in `{sample_id: {original, corrected}}` format
- `build_refined_dataset.py` — loads the original LINCE from Hugging Face and applies the refinements to reconstruct the refined set

## What this is NOT

- A replacement for LINCE — the original benchmark remains the canonical source
- A drop-in dataset — running the reproduction script is required to materialize the data

## How to use

```bash
# 1. Install dependencies
pip install datasets

# 2. Clone this repo
git clone https://huggingface.co/datasets/badashin/lince-sa-refined
cd lince-sa-refined

# 3. Run the reproduction script
python build_refined_dataset.py
# Output: refined_dataset.json  (5,567 samples)
```

The script downloads LINCE SA `sa_spaeng` via `datasets.load_dataset`, filters for genuine Spanish-English code-switching (both languages with ≥ 2 tokens each), applies the 763 refinements where the recorded original label still matches, and drops samples whose `sample_id` appears in more than one split.

## What's in the mapping

763 sentiment label refinements with the following transition distribution:

| Original | → | Refined  | Count | %     |
|----------|---|----------|------:|------:|
| positive | → | neutral  |   194 | 25.4% |
| neutral  | → | positive |   156 | 20.4% |
| neutral  | → | negative |   133 | 17.4% |
| positive | → | negative |   114 | 14.9% |
| negative | → | neutral  |    89 | 11.7% |
| negative | → | positive |    77 | 10.1% |

The dominant pattern is bidirectional positive ↔ neutral refinement (45.8%), reflecting cultural-pragmatic context — humor, formulaic greetings, sarcasm, and Hispanic-American social media conventions — where surface signals can mislead annotators less familiar with these registers.

## Methodology (brief)

- Sample-level review of LINCE SA `sa_spaeng` train + validation splits
- Selection criteria: genuine Spanish-English code-switching (≥ 2 tokens in each language)
- Annotation by a trilingual researcher (ES / EN / KO) using Hispanic-American social media norms
- Conservative labeling for ambiguous cases (preferring `neutral` over false positives)
- Final refined dataset: 5,567 code-switched samples after deduplication

Full methodology, illustrative examples, and downstream results are in the paper.

## Downstream impact

Controlled mBERT experiments using identical hyperparameters across original and refined data show **+4.0pp improvement** (56.6% → 60.6%) attributable to label refinement alone. A subsequent mBERT–XLM-R Late Fusion ensemble on the refined data reaches **67.15%**.

## Refined label distribution

After deduplication, the 5,567-sample refined set has the following label distribution:

| Label    | Count | Share |
|----------|------:|------:|
| positive | 3,021 | 54.3% |
| neutral  | 1,519 | 27.3% |
| negative | 1,027 | 18.4% |

## Citation

If you use this dataset, please cite both the original LINCE benchmark and this work:

```bibtex
@inproceedings{aguilar2020lince,
  title     = {LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation},
  author    = {Aguilar, Gustavo and Kar, Sudipta and Solorio, Thamar},
  booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
  year      = {2020}
}

@inproceedings{shin2025relabeling,
  title     = {Re-labeling Approach for Spanish-English Code-switching Sentiment Analysis: Impact of Data Quality Improvement},
  author    = {Shin, Bada and Kim, Sunoh},
  booktitle = {Korea Software Congress (KSC)},
  year      = {2025},
  note      = {Honorable Mention}
}
```

## Acknowledgments

This work builds on the **LINCE benchmark** (Aguilar et al., 2020), which provides invaluable infrastructure for code-switching research. The Spanish-English sentiment analysis subset (`sa_spaeng`) used here was originally curated by the LINCE team.

This research was supported by the SW-Centered University Project (2024-0-00035) of the Ministry of Science and ICT and the Institute of Information & Communications Technology Planning & Evaluation (IITP), Republic of Korea.

## License

- Label mapping (`label_mapping.json`) and reproduction script (`build_refined_dataset.py`) — **CC-BY-4.0**
- The original LINCE data is **not** redistributed and remains subject to its own terms (see https://ritual.uh.edu/lince/)

## Links

- 📄 Paper: https://github.com/vamosbada/project-puente/blob/main/docs/paper.pdf
- 💻 Full project (code, notebooks, experiments): https://github.com/vamosbada/project-puente
- 🏛️ Original LINCE benchmark: https://huggingface.co/datasets/lince-benchmark/lince