README.md

---
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](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.](https://arxiv.org/pdf/2410.20651)

## Citation Information
```bibtex
@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.

## GitHub Link
[Link to our GitHub repository.](https://github.com/gtfintechlab/SubjECTive-QA.git)