metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
languages:
  - en
licenses:
  - cc-by-nc-sa-3.0
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_Review

Dataset Card for financial_phrasebank

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Auditor review data collected by News Department

Point of Contact: Talked to COE for Auditing

Dataset Summary

Auditor sentiment dataset of sentences from financial news. The dataset consists of *** sentences from English language financial news categorized by sentiment. The dataset is divided by agreement rate of 5-8 annotators.

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', 'negative' or 'neutral'

Data Splits

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

Dataset Creation

Curation Rationale

The key arguments for the low utilization of statistical techniques in financial sentiment analysis have been the difficulty of implementation for practical applications and the lack of high quality training data for building such models. ***

Source Data

Initial Data Collection and Normalization

The corpus used in this paper is made out of English news on all listed companies in ****

Who are the source language producers?

The source data was written by various auditors

Annotations

Annotation process

This release of the financial phrase bank covers a collection of 4840 sentences. The selected collection of phrases was annotated by 16 people with adequate background knowledge on financial markets.

Given the large number of overlapping annotations (5 to 8 annotations per sentence), there are several ways to define a majority vote based gold standard. To provide an objective comparison, we have formed 4 alternative reference datasets based on the strength of majority agreement:

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset