metadata

language:
  - en
license: cc-by-nc-nd-4.0
size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
dataset_info:
  features:
    - name: Dates
      dtype: string
    - name: URL
      dtype: string
    - name: News
      dtype: string
    - name: Price Direction Up
      dtype: int64
    - name: Price Direction Constant
      dtype: int64
    - name: Price Direction Down
      dtype: int64
    - name: Asset Comparision
      dtype: int64
    - name: Past Information
      dtype: int64
    - name: Future Information
      dtype: int64
    - name: Price Sentiment
      dtype: string
  splits:
    - name: train
      num_bytes: 1948393.6
      num_examples: 8456
    - name: test
      num_bytes: 487098.4
      num_examples: 2114
  download_size: 923541
  dataset_size: 2435492
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
tags:
  - finance
  - news
  - NLP
  - Multiclass Classification

Dataset Card for Sentiment Analysis of Commodity News (Gold)

This is a news dataset for the commodity market which has been manually annotated for 10,000+ news headlines across multiple dimensions into various classes. The dataset has been sampled from a period of 20+ years (2000-2021). The dataset was curated by Ankur Sinha and Tanmay Khandait and is detailed in their paper "Impact of News on the Commodity Market: Dataset and Results." It is currently published by the authors on Kaggle, under the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

Dataset Descriptions

Homepage: Kaggle ResearchGate
Repository:
Paper: Arxiv

The Kaggle dataset consists of a 1.95MB CSV file, with 10 columns, and 10570 rows.

Uses

Sentiment Classification

Dataset Structure

Data Instances

{
  'Dates': '28-01-2016',
  'URL': 'http://www.marketwatch.com/story/april-gold-down-20-cents-to-settle-at-111610oz-2016-01-28',
  'News': 'april gold down 20 cents to settle at $1,116.10/oz',
  'Price Direction Up': 0, 'Price Direction Constant': 0,
  'Price Direction Down': 1,
  'Asset Comparision': 0,
  'Past Information': 1,
  'Future Information': 0,
  'Price Sentiment': 'negative'
}

Data Fields

Dates: Date of news headline
URL: URL of news headline
News: News headline
Price Direction Up: Does the news headline imply price direction up?
Price Direction Constant: Does the news headline imply price direction sideways (no change)?
Price Direction Down: Does the news headline imply price direction down?
Asset Comparision: Are assets being compared?
Past Information: Is the news headline talking about past?
Future Information: Is the news headline talking about future?
Price Sentiment: Price sentiment of Gold commodity based on headline

Data Splits

There is currently an train/test split of 80%/20%, with train split having 8456 elements and the test split having 2114 elements. Each of the dataset article entries contain a subset of the following features: "Price Direction Up", "Price Direction Constant", "Price Direction Down", "Asset Comparison", "Past Information", "Future Information", "Price Sentiment" Below are tables for each task, with the different categories separated into columns: Below is the README table for each task, with separate rows for train and test values:

Task	Train	Test
Price Direction Up	0: 4925 1: 3531	0: 1233 1: 881
Price Direction Constant	0: 8092 1: 364	0: 2034 1: 80
Price Direction Down	0: 5311 1: 3145	0: 1347 1: 767
Asset Comparison	0: 6855 1: 1601	0: 1714 1: 400
Past Information	1: 8196 0: 260	1: 2056 0: 58
Future Information	0: 8195 1: 261	0: 2056 1: 58
Price Sentiment	positive: 3531 negative: 3068 none: 1552 neutral: 305	positive: 881 negative: 746 none: 416 neutral: 71

Price Direction Up

	0	1
Train	4925	3531
Test	1233	881

Price Direction Constant

	0	1
Train	8092	364
Test	2034	80

Price Direction Down

	0	1
Train	5311	3145
Test	1347	767

Asset Comparison

	0	1
Train	6855	1601
Test	1714	400

Past Information

	1	0
Train	8196	260
Test	2056	58

Future Information

	0	1
Train	8195	261
Test	2056	58

Price Sentiment

	positive	negative	none	neutral
Train	3531	3068	1552	305
Test	881	746	416	71

Dataset Creation

Curation Rationale

Commodity prices are known to be quite volatile. Machine learning models that understand the commodity news well, will be able to provide an additional input to the short-term and long-term price forecasting models. The dataset will also be useful in creating news-based indicators for commodities.

Apart from researchers and practitioners working in the area of news analytics for commodities, the dataset will also be useful for researchers looking to evaluate their models on classification problems in the context of text-analytics. Some of the classes in the dataset are highly imbalanced and may pose challenges to the machine learning algorithms.

Source

Source Data

The source data is news text and headlines.

Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Dataset and Results." In Future of Information and Communication Conference, pp. 589-601. Springer, Cham, 2021.

Source

Data Collection and Processing

The dataset has been collected from various news sources and annotated by three human annotators who were subject experts. Each news headline was evaluated on various dimensions, for instance - if a headline is a price related news then what is the direction of price movements it is talking about; whether the news headline is talking about the past or future; whether the news item is talking about asset comparison; etc.

Source

Kaggle Datatset Description Source

Context

This is a news dataset for the commodity market where we have manually annotated 10,000+ news headlines across multiple dimensions into various classes. The dataset has been sampled from a period of 20+ years (2000-2021).

Content

Acknowledgements

Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Dataset and Results." In Future of Information and Communication Conference, pp. 589-601. Springer, Cham, 2021.

https://arxiv.org/abs/2009.04202

Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Dataset and Results." arXiv preprint arXiv:2009.04202 (2020)

We would like to acknowledge the financial support provided by the India Gold Policy Centre (IGPC).

Inspiration

Citation

@misc{sinha2020impactnewscommoditymarket,
      title={Impact of News on the Commodity Market: Dataset and Results}, 
      author={Ankur Sinha and Tanmay Khandait},
      year={2020},
      eprint={2009.04202},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2009.04202}, 
}

Dataset Card Authors

Saguaro Capital Management, LLC

Dataset Card Contact

Tyler Thomas: [email protected]