Update README.md

README.md

@@ -51,10 +51,7 @@ tags:
 - Multiclass Classification
 ---
 # Dataset Card for Sentiment Analysis of Commodity News (Gold)
-
-<!-- Provide a quick summary of the dataset. -->
-
-This is a news dataset for the commodity market which has been 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).
+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
@@ -65,8 +62,6 @@ The dataset was curated by Ankur Sinha and Tanmay Khandait and is detailed in th
 The Kaggle dataset consists of a 1.95MB CSV file, with 10 columns, and 10570 rows.
 
 ## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
 Sentiment Classification
 
 ## Dataset Structure
@@ -101,15 +96,19 @@ Sentiment Classification
 
 ### Data Splits
 There is currently an train/test split of 80%/20%. 
+- The train split has 8456 elements. The different counts of "Price Sentiment" within this split are as follows. 'positive': 3531; 'negative': 3068; 'none': 1552; 'neutral': 305.
+- The test split has 2114 elements. The different counts of "Price Sentiment" within this split are as follows. 'positive': 881; 'negative': 746; 'none': 416; 'neutral': 71.
 
 ## Dataset Creation
 
 ### Curation Rationale
 
 <!-- Motivation for the creation of this dataset. -->
-"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.
+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."
+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
 
@@ -117,11 +116,14 @@ Apart from researchers and practitioners working in the area of news analytics f
 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.
-[Quotation source](https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold/data)
+
+[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."
+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)
 <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
 
 <!-- #### Annotation process
@@ -154,9 +156,28 @@ Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Data
 
 <!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. --> 
 
-## Citation
+## 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
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+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}, 
@@ -169,18 +190,8 @@ Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Data
 }
 ```
 
-<!-- ## Glossary [optional]
- -->
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-<!-- [More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed] -->
-
 ## Dataset Card Authors
 Saguaro Capital Management, LLC
 
-<!-- ## Dataset Card Contact
-[https://www.saguarocm.com/](https://www.saguarocm.com/) -->
+## Dataset Card Contact
+Tyler Thomas: [tyler@saguarocm.com](mailto:tyler@saguarocm.com)