nahid-hub/BLUGE-bengali-sentiment-classification

BLUGE-TSC: Bangla Sentiment Classification

BLUGE-TSC is a meticulously curated and cleaned Bangla Ternary Sentiment Classification dataset, one of the 7 tasks in BLUGE (Bengali Language UnderstandinG Evaluation), a balanced benchmark for evaluating Bengali natural language understanding. See the full BLUGE collection for all 7 tasks, and the B-CORE pretraining corpus and BnLM model suite released alongside it.

Dataset Description

This task classifies Bangla text into three sentiment labels:

Label	Sentiment
0	Neutral
1	Positive
2	Negative

The dataset was constructed by merging two existing sentiment corpora and subjecting them to rigorous orthographic cleaning and class-balancing. Orthographic errors present in 7.23% of samples were flagged, and corrections were proposed using GPT-4-turbo — used exclusively for orthographic correction, with no role in sentiment label assignment. Every proposed correction was independently reviewed and accepted, revised, or rejected by two native Bengali linguists (inter-annotator agreement κ=0.87), with disagreements resolved by a third linguist. Of the proposals reviewed, 76.4% were accepted as-is, 15.8% were revised before acceptance, and 7.8% were rejected and reverted to the original text — final semantic preservation relative to the original texts was verified at 98.9%.

Sentences shorter than 4 words or longer than 20 words were excluded. To correct severe class imbalance in the merged corpus, 1,247 additional samples were manually collected and annotated across underrepresented sentiment classes, validated by the same expert annotators (κ=0.89).

Dataset Structure

Balanced 80 / 10 / 10 split:

Split	Samples
Train	17,836
Validation	2,230
Test	2,230
Total	22,296

Fields:

• text — the Bangla sentence
• label — sentiment class (0: neutral, 1: positive, 2: negative)

Usage

from datasets import load_dataset

# Load all splits
ds = load_dataset("nahid-hub/BLUGE-bengali-sentiment-classification")

# Access a specific split
train_ds = ds["train"]
val_ds   = ds["validation"]
test_ds  = ds["test"]

print(train_ds[0])

Load a single split directly:

from datasets import load_dataset

test_ds = load_dataset("nahid-hub/BLUGE-bengali-sentiment-classification", split="test")

Or read the Parquet files directly with pandas:

import pandas as pd

train_df = pd.read_parquet(
    "hf://datasets/nahid-hub/BLUGE-bengali-sentiment-classification/train.parquet"
)

Annotation Process

• Orthographic correction: GPT-4-turbo proposals, human-reviewed only — never used for label assignment
• Inter-annotator agreement: κ=0.87 (correction review), κ=0.89 (new sample annotation)
• Class balancing: 1,247 manually annotated samples added for underrepresented classes
• Semantic fidelity: 98.9% preserved relative to original texts after correction

License

Released under CC BY 4.0. You are free to share and adapt this dataset for any purpose, including commercially, provided you give appropriate credit. Note that merged source components may carry their own attribution requirements — see Source Data above.

Citation

If you use this dataset, please cite:

@ARTICLE{BnLM-BLUGE-B-CORE,
  author={Hossain, Nahid and Faisal Kabir, Md.},
  journal={IEEE Access}, 
  title={Efficient Monolingual Pretraining in Low-Resource Settings Through Morphology-Aware Tokenization, Principled Corpus Denoising, and Benchmark-Driven Evaluation}, 
  year={2026},
  volume={14},
  number={},
  pages={91979-92003},
  keywords={Modeling;Multilingual;Training;Cleaning;Vocabulary;Labeling;Tokenization;Computational linguistics;Pipelines;Conferences;B-CORE;BLUGE;BnLM;corpus;evaluation benchmark;pretrained models},
  doi={10.1109/ACCESS.2026.3701520}
}