README.md

---
title: 'LexCAT: Taglish Sentiment Analysis'
colorFrom: blue
colorTo: purple
sdk: gradio
app_file: app.py
pinned: true
license: cc-by-4.0
sdk_version: 5.47.0
---

# LexCAT: Lexicon-Enhanced Sentiment Analysis for Tagalog–English Code-Switched Text

**Author**: Glenn Marcus D. Cinco  
**Institution**: Mapúa University  
**Thesis**: *LexCAT: A Lexicon-Based Approach for Code-Switching Analysis with Transformers Using XLM-RoBERTa and LexiLiksik*  
**Model**: Fine-tuned XLM-RoBERTa + LexiLiksik lexicon  
**Dataset**: FiReCS (10,487 Taglish reviews)  
**Accuracy**: 84.31%  
**Specialty**: Detects intra-sentential sentiment shifts (e.g., “Maganda pero expensive” → Negative)

---

## 🧠 Try It Out

Enter any Taglish sentence — LexCAT will predict its sentiment and show confidence scores.

Examples:
- *“sobrang lambot ng burger pero expensive tlga”* → ❌ Negative
- *“Salamat sa nyo nagana nmn po sya kaya super thank you ako”* → ✅ Positive
- *“Ang ganda ng service, one star!”* → ❌ Negative

---

## 📚 Model Card

See full documentation, methodology, and citation at:  
👉 https://huggingface.co/your-hf-username/LexCAT-LexiLiksik-Final

---

## 🎓 Academic Use

Cite this model in your research:

```bibtex
@mastersthesis{cinco2025lexcat,
  author = {Cinco, Glenn Marcus D.},
  title = {LexCAT: A Lexicon-Based Approach for Code-Switching Analysis with Transformers Using XLM-RoBERTa and LexiLiksik},
  school = {Mapúa University},
  year = {2025}
}
```