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… See the full description on the dataset page: https://huggingface.co/datasets/lmassaron/FinancialPhraseBank.

Dataset

This dataset was created by Daniel M.

Released under CC0: Public Domain

Contents

Dataset Card for "financial-phrasebank-all-agree-classification"

More Information needed

Data

The following data is intended for advancing financial sentiment analysis research. It's two datasets (FiQA, Financial PhraseBank) combined into one easy-to-use CSV file. It provides financial sentences with sentiment labels.

Citations

Malo, Pekka, et al. "Good debt or bad debt: Detecting semantic orientations in economic texts." Journal of the Association for Information Science and Technology 65.4 (2014): 782-796.

Dataset

This dataset was created by MukhammedAbuSuveilim

Contents

Dataset Creation

This dataset combines financial phrasebank dataset and a financial text dataset from Kaggle. Given the financial phrasebank dataset does not have a validation split, I thought this might help to validate finance models and also capture the impact of COVID on financial earnings with the more recent Kaggle dataset.

Dataset Card for "financial-phrasebank-all-agree-clustering"

More Information needed

Enhancing Financial Market Predictions: Causality-Driven Feature Selection

Note:[Please help give a Vote 👍 if you think this FinSen dataset is good for you, Thanks:)]

This paper introduces FinSen dataset that revolutionizes financial market analysis by integrating economic and financial news articles from 197 countries with stock market data. The dataset’s extensive coverage spans 15 years from 2007 to 2023 with temporal information, offering a rich, global perspective 160,000 records on financial market news. Our study leverages causally validated sentiment scores and LSTM models to enhance market forecast accuracy and reliability.

Technical Framework

https://github.com/user-attachments/assets/5df3c4a7-2403-460a-ac7f-2d69572fec2f" alt="image">

Our FinSen Dataset

This repository contains the dataset for "https://arxiv.org/abs/2408.01005">Enhancing Financial Market Predictions: Causality-Driven Feature Selection, which has been accepted in ADMA 2024.

If the dataset or the paper has been useful in your research, please add a citation to our work:

@article{liang2024enhancing,
 title={Enhancing Financial Market Predictions: Causality-Driven Feature Selection},
 author={Liang, Wenhao and Li, Zhengyang and Chen, Weitong},
 journal={arXiv e-prints},
 pages={arXiv--2408},
 year={2024}
}

Datasets

[FinSen] can be downloaded manually from the repository as csv file. Sentiment and its score are generated by FinBert model from the Hugging Face Transformers library under the identifier "ProsusAI/finbert". (Araci, Dogu. "Finbert: Financial sentiment analysis with pre-trained language models." arXiv preprint arXiv:1908.10063 (2019).)

We only provide US for research purpose usage, please contact w.liang@adelaide.edu.au for other countries (total 197 included) if necessary.

https://github.com/user-attachments/assets/f28e670a-7329-409d-81cb-1fe47da22140" alt="image">

Finsen Data Sample:

https://github.com/user-attachments/assets/6ab08486-85b7-4cf6-b4fe-7d4294624f91">

We also provide other NLP datasets for text classification tasks here, please cite them correspondingly once you used them in your research if any.

1. 20Newsgroups. Joachims, T., et al.: A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In: ICML. vol. 97, pp. 143–151. Citeseer (1997)
2. AG News. Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. Advances in neural information processing systems 28 (2015)
3. Financial PhraseBank. Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Takala, P.: Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology 65(4), 782–796 (2014)

Dataloader for FinSen

We provide the preprocessing file finsen.py for our FinSen dataset under dataloaders directory for more convienient usage.

Models - Text Classification

1. DAN-3.

2. Gobal Pooling CNN.

Models - Regression Prediction

1. LSTM

Using Sentiment Score from FinSen Predict Result on S&P500

https://github.com/user-attachments/assets/2d9b4dd7-7f59-425c-b812-2cca57719243" alt="image">

:smiley: ☺ Happy Research !

Dataset Card for "financial_phrasebank"

64/16/20 Split of the sentences_50agree subset of financial_phrasebank, according to the FinBERT paper.

intanm/indonesian-financial-phrasebank dataset hosted on Hugging Face and contributed by the HF Datasets community

Stock Market News Data in Portuguese

The Financial Phrase Bank is a dataset originally developed for the paper Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts, made available by researchers from Aalto University and the Indian Institute of Management. The dataset allows for a useful benchmark for fine-tuning Language Models on Sentiment Analysis Tasks.

As the amount of annotated text data (especially about the financial market) in Portuguese, I went ahead and translated the entire dataset for people to try out Sentiment Analysis tasks in Portuguese.

Content

The dataset originally contains about 4840 manually annotated financial news in English and consists of three columns: 1. y: the annotated label for the sentiment of the news text (neutral, positive, negative); 2. text: the original text for each record; 3. text_pt: the translated and that I manually validated version of the original record;

Acknowledgments

[1] Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014). Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology, 65(4), 782-796.

Photo by Markus Winkler on Unsplash

Dataset

This dataset was created by Pixel_Dust_64

Released under Apache 2.0

Contents

Aggregated Financial Reasoning Dataset for Reinforcement Fine Tuning(RFT) in Finance

A multi-source NLP dataset combining Financial PhraseBank, FinQA, news Headlines, and Twitter data, labeled for sentiment and QA tasks.

  I don't own any of the datasets, just curating for my own reasoning experiments and teaching materials.





  Dataset Overview

PurposeThis dataset is an aggregation of text sources that have a discrete output, which allows downstream RFT while… See the full description on the dataset page: https://huggingface.co/datasets/neoyipeng/financial_reasoning_aggregated.

Dataset

This dataset was created by Daniele Simas

Released under MIT

Contents

Dataset Card for Multilingual Financial Sentiment Analysis

This dataset is based in the combination of two datasets, FiQA and Financial PhraseBank, automatically translated to spanish, french and german.

  Dataset Details





  Dataset Sources

KaggleHub: Financial Sentiment Analysis Paper: Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts Financial Opinion Mining and Question Answering

  Uses

Multilingual financial sentiment… See the full description on the dataset page: https://huggingface.co/datasets/nojedag/financial_phrasebank_multilingual.

Dataset Card for German financial_phrasebank

  Dataset Description





  Dataset Summary

This datset is a German translation of the financial phrasebank of Malo et al. (2013) with a minimum agreement rate between annotators of 75% (3453 observations in total). The translation was mechanically accomplished with Deepl.

  Supported Tasks and Leaderboards

Sentiment Classification

  Languages

German

  Dataset Structure





  Data Instances

{… See the full description on the dataset page: https://huggingface.co/datasets/scherrmann/financial_phrasebank_75agree_german.