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--- |
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annotations_creators: |
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- expert-generated |
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language_creators: |
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- found |
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language: |
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- en |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- original |
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task_categories: |
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- text-classification |
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task_ids: |
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- multi-class-classification |
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- sentiment-classification |
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paperswithcode_id: null |
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pretty_name: Auditor_Review |
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--- |
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# Dataset Card for Auditor_Review |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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## Dataset Description |
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Auditor review data collected by News Department |
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- **Point of Contact:** |
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Talked to COE for Auditing, currently [email protected] |
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### Dataset Summary |
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Auditor sentiment dataset of sentences from financial news. The dataset consists of 3500 sentences from English language financial news categorized by sentiment. The dataset is divided by the agreement rate of 5-8 annotators. |
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### Supported Tasks and Leaderboards |
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Sentiment Classification |
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### Languages |
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English |
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## Dataset Structure |
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### Data Instances |
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``` |
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"sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .", |
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"label": "negative" |
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``` |
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### Data Fields |
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- sentence: a tokenized line from the dataset |
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- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0) |
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Complete data code is [available here](https://www.datafiles.samhsa.gov/get-help/codebooks/what-codebook) |
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### Data Splits |
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A train/test split was created randomly with a 75/25 split |
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## Dataset Creation |
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### Curation Rationale |
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To gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance. |
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### Source Data |
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#### Initial Data Collection and Normalization |
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The corpus used in this paper is made out of English news reports. |
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#### Who are the source language producers? |
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The source data was written by various auditors. |
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### Annotations |
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#### Annotation process |
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This release of the auditor reviews covers a collection of 4840 |
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sentences. The selected collection of phrases was annotated by 16 people with |
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adequate background knowledge of financial markets. The subset here is where inter-annotation agreement was greater than 75%. |
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#### Who are the annotators? |
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They were pulled from the SME list, names are held by [email protected] |
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### Personal and Sensitive Information |
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There is no personal or sensitive information in this dataset. |
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## Considerations for Using the Data |
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### Discussion of Biases |
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All annotators were from the same institution and so interannotator agreement |
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should be understood with this taken into account. |
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### Other Known Limitations |
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[More Information Needed] |
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### Licensing Information |
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License: Demo.Org Proprietary - DO NOT SHARE |
