README.md

---
license: mit
task_categories:
- text-classification
language:
- my
tags:
- sentiment
- myanmar
- burmese
pretty_name: Myanmar Sentiment Intensity Dataset v1
size_categories:
- 1K<n<10K
---

# Myanmar Sentiment Intensity Dataset v1

This dataset contains Myanmar (Burmese) text samples annotated for sentiment intensity across 5 levels. It is specifically curated for training and evaluating text classification models.

## Dataset Structure
The dataset consists of two primary columns:
- **text**: Raw Myanmar text strings.
- **label**: Sentiment intensity score (Integer 0 to 4).

### Label Definitions
| Label | Emoji | Sentiment | Description |
|---|---|---|---|
| 0 | 🔴 | Very Negative | Strong dissatisfaction, anger, or hate |
| 1 | 🟠 | Negative | Dissatisfaction, boredom, or sadness |
| 2 | 🟡 | Neutral | Normal, factual, or indifferent |
| 3 | 🟢 | Positive | Satisfied, good, or peaceful |
| 4 | 🔵 | Very Positive | Very happy, excellent, or joyful |

## Data Preprocessing
To achieve optimal results with this dataset, it is highly recommended to perform **Syllable Breaking** before tokenization. This ensures the model processes the text using Myanmar syllable units.

### Syllable Breaking Logic:
The regex implementation is based on the [ye-kyaw-thu/sylbreak](https://github.com/ye-kyaw-thu/sylbreak) algorithm.

```python
import re

def myanmar_sylbreak(line):
    pat = re.compile(r"((?<!္)[က-အ](?![်္])|[a-zA-Z0-9ဣဤဥဦဧဩဪဿ၌၍၏၀-၉၊။!-/:-@[-`{-~\s])")
    line = re.sub(r'\s+', ' ', str(line).strip())
    return pat.sub(r" \1", line).strip()

```

## Acknowledgments & Credits

This dataset is developed by building upon existing Myanmar NLP resources and research:

* **Base Dataset Source:** This work utilizes data and concepts from the [Myanmar Text Segmentation Dataset](https://huggingface.co/datasets/chuuhtetnaing/myanmar-social-media-sentiment-analysis-dataset) by Chuu Htet Naing.
* **Syllable Segmentation:** The syllable breaking logic is based on the [sylbreak](https://github.com/ye-kyaw-thu/sylbreak) repository by Ye Kyaw Thu.