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metadata
dataset_info:
  features:
    - name: ID
      dtype: string
    - name: Text
      dtype: string
    - name: Polarity
      dtype: string
    - name: Domain
      dtype: string
  splits:
    - name: train
      num_bytes: 15164685
      num_examples: 70000
  download_size: 7415117
  dataset_size: 15164685
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
tags:
  - sentigold
  - bangla
  - bangladataset
  - sentiment

Bangla Sentiment Dataset (SentiGOLD v1)

SentiGOLD 70k v1 A multi-domain sentiment analysis dataset in Bangla is called SentiGOLD. A gender-balanced team of linguists annotated the 70,000 samples, which were obtained from a variety of sources. SentiGOLD complies with language standards that have been developed by a Bangla linguistics commission and the Government of Bangladesh.

Each text entry in the dataset is categorized into one of the following sentiment classes:

  • SP: Strongly Positive
  • WP: Weakly Positive
  • WN: Weakly Positive Negative
  • SN: Strongly Negative
  • NU: Neutral

This dataset provides a valuable resource for building and evaluating sentiment analysis models in the Bangla language.

Use the Dataset

from datasets import load_dataset

dataset = load_dataset('SayedShaun/sentigold')
print(dataset)

>>> DatasetDict({
>>>     train: Dataset({
>>>         features: ['ID', 'Text', 'Polarity', 'Domain'],
>>>         num_rows: 70000
>>>    })
>>> })

Source and Citation

SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and Its Evaluation

@inproceedings{islam2023sentigold,
  title={Sentigold: A large bangla gold standard multi-domain sentiment analysis dataset and its evaluation},
  author={Islam, Md Ekramul and Chowdhury, Labib and Khan, Faisal Ahamed and Hossain, Shazzad and Hossain, Md Sourave and Rashid, Mohammad Mamun Or and Mohammed, Nabeel and Amin, Mohammad Ruhul},
  booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={4207--4218},
  year={2023}
}