dataset_info:
- config_name: '5768'
features:
- name: COMPANYNAME
dtype: string
- name: QUARTER
dtype: string
- name: YEAR
dtype: int64
- name: ASKER
dtype: string
- name: RESPONDER
dtype: string
- name: QUESTION
dtype: string
- name: ANSWER
dtype: string
- name: CLEAR
dtype: int64
- name: ASSERTIVE
dtype: int64
- name: CAUTIOUS
dtype: int64
- name: OPTIMISTIC
dtype: int64
- name: SPECIFIC
dtype: int64
- name: RELEVANT
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2216875
num_examples: 1922
- name: test
num_bytes: 662070
num_examples: 577
- name: val
num_bytes: 287178
num_examples: 248
download_size: 1723955
dataset_size: 3166123
- config_name: '78516'
features:
- name: COMPANYNAME
dtype: string
- name: QUARTER
dtype: string
- name: YEAR
dtype: int64
- name: ASKER
dtype: string
- name: RESPONDER
dtype: string
- name: QUESTION
dtype: string
- name: ANSWER
dtype: string
- name: CLEAR
dtype: int64
- name: ASSERTIVE
dtype: int64
- name: CAUTIOUS
dtype: int64
- name: OPTIMISTIC
dtype: int64
- name: SPECIFIC
dtype: int64
- name: RELEVANT
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2223784
num_examples: 1922
- name: test
num_bytes: 654430
num_examples: 577
- name: val
num_bytes: 287909
num_examples: 248
download_size: 1722234
dataset_size: 3166123
- config_name: '944601'
features:
- name: COMPANYNAME
dtype: string
- name: QUARTER
dtype: string
- name: YEAR
dtype: int64
- name: ASKER
dtype: string
- name: RESPONDER
dtype: string
- name: QUESTION
dtype: string
- name: ANSWER
dtype: string
- name: CLEAR
dtype: int64
- name: ASSERTIVE
dtype: int64
- name: CAUTIOUS
dtype: int64
- name: OPTIMISTIC
dtype: int64
- name: SPECIFIC
dtype: int64
- name: RELEVANT
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2197260
num_examples: 1922
- name: test
num_bytes: 671149
num_examples: 577
- name: val
num_bytes: 297714
num_examples: 248
download_size: 1713897
dataset_size: 3166123
configs:
- config_name: '5768'
data_files:
- split: train
path: 5768/train-*
- split: test
path: 5768/test-*
- split: val
path: 5768/val-*
- config_name: '78516'
data_files:
- split: train
path: 78516/train-*
- split: test
path: 78516/test-*
- split: val
path: 78516/val-*
- config_name: '944601'
data_files:
- split: train
path: 944601/train-*
- split: test
path: 944601/test-*
- split: val
path: 944601/val-*
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- finance
pretty_name: SubjECTive-QA
size_categories:
- 10K<n<100K
Dataset Summary
For dataset summary, please refer to https://huggingface.co/datasets/gtfintechlab/subjectiveqa
Additional Information
This dataset is annotated across six subjective dimensions: Assertive, Cautious, Optimistic, Specific, Clear, and Relevant. It contains 2,747 longform QA pairs taken from the Earnings Call Transcripts of 120 companies listed on the NYSE from 2007-2021.
Label Interpretation (e.g. CLEAR)
0: Negatively Demonstrative of the dimension (e.g. CLEAR)
Indicates that the response lacks clarity.1: Neutral Demonstration of 'the dimension (e.g. CLEAR)
Indicates that the response has an average level of clarity.2: Positively Demonstrative of the dimension (e.g. CLEAR)
Indicates that the response is clear and transparent.
Licensing Information
The SubjECTive-QA dataset is licensed under the Creative Commons Attribution 4.0 International License. More information in the paper.
Citation Information
@misc{pardawala2024subjectiveqameasuringsubjectivityearnings,
title={SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis},
author={Huzaifa Pardawala and Siddhant Sukhani and Agam Shah and Veer Kejriwal and Abhishek Pillai and Rohan Bhasin and Andrew DiBiasio and Tarun Mandapati and Dhruv Adha and Sudheer Chava},
year={2024},
eprint={2410.20651},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.20651},
}
Contact
Please contact Huzaifa Pardawala (huzaifahp7[at]gatech[dot]edu) or Agam Shah (ashah482[at]gatech[dot]edu) about any SubjECTive-QA related issues and questions.