ArtGarfunkel/FinancialPhraseBank

---
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](https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.23062)

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:

```bibtex
@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}
}