--- language: - en license: cc-by-nc-nd-4.0 size_categories: - 10K ### 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: $$ \begin{array}{|l|l|l|l|} \hline \textbf{Task} & \textbf{Category} & \textbf{Train} & \textbf{Test} \\ \hline \textbf{Price Direction Up} & 0 & 4925 & 1233 \\ & 1 & 3531 & 881 \\ \hline \textbf{Price Direction Constant} & 0 & 8092 & 2034 \\ & 1 & 364 & 80 \\ \hline \textbf{Price Direction Down} & 0 & 5311 & 1347 \\ & 1 & 3145 & 767 \\ \hline \textbf{Asset Comparison} & 0 & 6855 & 1714 \\ & 1 & 1601 & 400 \\ \hline \textbf{Past Information} & 1 & 8196 & 2056 \\ & 0 & 260 & 58 \\ \hline \textbf{Future Information} & 0 & 8195 & 2056 \\ & 1 & 261 & 58 \\ \hline \textbf{Price Sentiment} & \text{positive} & 3531 & 881 \\ & \text{negative} & 3068 & 746 \\ & \text{none} & 1552 & 416 \\ & \text{neutral} & 305 & 71 \\ \hline \end{array} $$ ### Price Direction Up ```latex $$ \begin{array}{|l|l|l|} \hline \textbf{Category} & \textbf{Train} & \textbf{Test} \\ \hline 0 & 4925 & 1233 \\ 1 & 3531 & 881 \\ \hline \end{array} $$ ``` ### Price Direction Constant ```latex $$ \begin{array}{|l|l|l|} \hline \textbf{Category} & \textbf{Train} & \textbf{Test} \\ \hline 0 & 8092 & 2034 \\ 1 & 364 & 80 \\ \hline \end{array} $$ ``` ### Price Direction Down ```latex $$ \begin{array}{|l|l|l|} \hline \textbf{Category} & \textbf{Train} & \textbf{Test} \\ \hline 0 & 5311 & 1347 \\ 1 & 3145 & 767 \\ \hline \end{array} $$ ``` ### Asset Comparison ```latex $$ \begin{array}{|l|l|l|} \hline \textbf{Category} & \textbf{Train} & \textbf{Test} \\ \hline 0 & 6855 & 1714 \\ 1 & 1601 & 400 \\ \hline \end{array} $$ ``` ### Past Information ```latex $$ \begin{array}{|l|l|l|} \hline \textbf{Category} & \textbf{Train} & \textbf{Test} \\ \hline 1 & 8196 & 2056 \\ 0 & 260 & 58 \\ \hline \end{array} $$ ``` ### Future Information ```latex $$ \begin{array}{|l|l|l|} \hline \textbf{Category} & \textbf{Train} & \textbf{Test} \\ \hline 0 & 8195 & 2056 \\ 1 & 261 & 58 \\ \hline \end{array} $$ ``` | 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 | ## 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](https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold/data) ### 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](https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold/data) #### 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](https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold/data) ## Kaggle Datatset Description [Source](https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold/data) ### 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](mailto:tyler@saguarocm.com)