SaguaroCapital/sentiment-analysis-in-commodity-market-gold

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
• Repository:
• Paper: Arxiv ResearchGate

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 is the table for each task, with separate columns for train and test splits:

Task	train	test
Price Direction Up	0: 4925 (58%) 1: 3531 (42%)	0: 1233 (58%) 1: 881 (42%)
Price Direction Constant	0: 8101 (96%) 1: 355 (4%)	0: 2025 (96%) 1: 89 (4%)
Price Direction Down	0: 5327 (63%) 1: 3129 (37%)	0: 1331 (63%) 1: 783 (37%)
Asset Comparison	0: 6855 (81%) 1: 1601 (19%)	0: 1714 (81%) 1: 400 (19%)
Past Information	0: 249 (3%) 1: 8207 (97%)	0: 69 (3%) 1: 2045 (97%)
Future Information	0: 8206 (97%) 1: 250 (3%)	0: 2045 (97%) 1: 69 (3%)
Price Sentiment	positive: 3531 (42%) negative: 3050 (36%) none: 1574 (19%) neutral: 301 (4%)	positive: 881 (42%) negative: 764 (36%) none: 394 (19%) neutral: 75 (4%)

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

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.

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

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.

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: tyler@saguarocm.com