annotations_creators: - expert-generated language_creators: - found language: - en multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification - sentiment-classification paperswithcode_id: null pretty_name: Auditor_Sentiment
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Auditor review sentiment collected by News Department
- Point of Contact: Talked to COE for Auditing, currently firstname.lastname@example.org
Auditor sentiment dataset of sentences from financial news. The dataset consists of several thousand sentences from English language financial news categorized by sentiment.
"sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .", "label": "negative"
- sentence: a tokenized line from the dataset
- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0)
A train/test split was created randomly with a 75/25 split
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.
The corpus used in this paper is made out of English news reports.
The source data was written by various auditors.
This release of the auditor reviews covers a collection of 4840 sentences. The selected collection of phrases was annotated by 16 people with adequate background knowledge on financial markets. The subset here is where inter-annotation agreement was greater than 75%.
They were pulled from the SME list, names are held by email@example.com
There is no personal or sensitive information in this dataset.
[More Information Needed]
All annotators were from the same institution and so interannotator agreement should be understood with this taken into account.
License: Demo.Org Proprietary - DO NOT SHARE
This dataset is based on the financial phrasebank dataset.