takala/financial_phrasebank

Dataset Overview

• Name: FinancialPhrasebank
• Language: English
• License: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License
• Multilinguality: Monolingual
• Size: 1K<n<10K
• Source Dataset: Original Data
• Task Category: Text Classification
• Task ID: Multi‑class Classification, Sentiment Classification
• Label Creator: Expert Generated
• Language Creator: Discovery

Dataset Structure

Data Instance

json { "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: The tokenized sentence in the dataset, data type is string.
• label: The label corresponding to the class, data type is categorical label, categories include negative, neutral, positive.

Data Split

• sentences_allagree: All annotators (100% agreement), 2,264 instances.
• sentences_75agree: >=75% annotator agreement, 3,453 instances.
• sentences_66agree: >=66% annotator agreement, 4,217 instances.
• sentences_50agree: >=50% annotator agreement, 4,846 instances.

Dataset Creation

Source Data

• Initial Data Collection and Normalization: English financial news downloaded from the LexisNexis database; 10,000 articles were randomly selected, resulting in approximately 5,000 sentences after filtering.
• Source Language Producers: Multiple financial journalists.

Annotation

• Annotation Process: 4,840 sentences were annotated by 16 individuals with financial background knowledge.
• Annotators: Three researchers and thirteen master's students from Aalto University's School of Business, primarily specializing in finance, accounting, and economics.

Dataset Usage Considerations

• Bias Discussion: All annotators are from the same institution, so annotator consistency should consider this factor.