README.md

metadata

annotations_creators:
  - expert-annotated
language_creators:
  - found
language:
  - en
license:
  - cc-by-nc-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 1k<n<10k
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
paperswithcode_id: financial-phrasebank
pretty_name: Financial PhraseBank
dataset_info:
  features:
    - name: sentiment
      dtype: string
    - name: sentence
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': negative
            '1': neutral
            '2': positive
  splits:
    - name: train
      num_bytes: 586208
      num_examples: 3872
    - name: validation
      num_bytes: 73996
      num_examples: 484
    - name: test
      num_bytes: 73088
      num_examples: 484
  download_size: 417897
  dataset_size: 733292
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for Financial PhraseBank

Dataset Description

Repository: [Link to the source, e.g., on Kaggle or original paper's site] Paper: Good debt or bad debt: Detecting semantic orientations in economic texts

This dataset (FinancialPhraseBank) contains the sentiments for 4846 financial news headlines from the perspective of a retail investor. The dataset is labeled with "negative", "neutral", or "positive" sentiments.

Content

The dataset contains two columns:

• sentiment: The sentiment label (negative, neutral, or positive).
• sentence: The news headline text.

Intended Uses

This dataset is primarily intended for training and evaluating sentiment analysis models, specifically in the financial domain. It can be used for:

• Supervised fine-tuning of language models.
• Benchmarking text classification models.
• Research into financial text semantics.

Acknowledgements

This dataset was created by the authors of the following paper. Please cite them if you use this dataset in your work:

@article{Malo2014GoodDO,
  title={Good debt or bad debt: Detecting semantic orientations in economic texts},
  author={Pekka Malo and Ankur Sinha and Pekka Korhonen and Jyrki Wallenius and Pasi Takala},
  journal={Journal of the Association for Information Science and Technology},
  year={2014},
  volume={65},
  pages={782-796}
}