metadata
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 algorithm.
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 by Chuu Htet Naing.
- Syllable Segmentation: The syllable breaking logic is based on the sylbreak repository by Ye Kyaw Thu.