auditor_sentiment / README.md
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metadata
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

Dataset Card for Auditor Sentiment

Table of Contents

Dataset Description

Auditor review sentiment collected by News Department

  • Point of Contact: Talked to COE for Auditing, currently sue@demo.org

Dataset Summary

Auditor sentiment dataset of sentences from financial news. The dataset consists of several thousand sentences from English language financial news categorized by sentiment.

Supported Tasks and Leaderboards

Sentiment Classification

Languages

English

Dataset Structure

Data Instances

"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"

Data Fields

  • sentence: a tokenized line from the dataset
  • label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0)

Data Splits

A train/test split was created randomly with a 75/25 split

Dataset Creation

Curation Rationale

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.

Source Data

Initial Data Collection and Normalization

The corpus used in this paper is made out of English news reports.

Who are the source language producers?

The source data was written by various auditors.

Annotations

Annotation process

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%.

Who are the annotators?

They were pulled from the SME list, names are held by sue@demo.org

Personal and Sensitive Information

There is no personal or sensitive information in this dataset.

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

All annotators were from the same institution and so interannotator agreement should be understood with this taken into account.

Licensing Information

License: Demo.Org Proprietary - DO NOT SHARE

This dataset is based on the financial phrasebank dataset.