Datasets:

amphora
/

korfin-asc

	---
	annotations_creators:
	- expert-generated
	language:
	- ko
	language_creators:
	- expert-generated
	license: cc-by-sa-4.0
	multilinguality:
	- monolingual
	pretty_name: KorFin-ABSA
	size_categories:
	- 1K<n<10K
	source_datasets:
	- klue
	tags:
	- sentiment analysis
	- aspect based sentiment analysis
	- finance
	task_categories:
	- text-classification
	task_ids:
	- topic-classification
	- sentiment-classification
	---

	# Dataset Card for KorFin-ABSA

	## Table of Contents
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description
	### Dataset Summary

	The KorFin-ASC is an extension of KorFin-ABSA including 8818 samples with (aspect, polarity) pairs annotated.
	The samples were collected from [KLUE-TC](https://klue-benchmark.com/tasks/66/overview/description) and
	analyst reports from [Naver Finance](https://finance.naver.com).
	Annotation of the dataset is described in the paper [Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance](https://arxiv.org/abs/2301.03136).


	### Supported Tasks and Leaderboards

	This dataset supports the following tasks:

	* Aspect-Based Sentiment Classification

	### Languages

	Korean

	## Dataset Structure

	### Data Instances

	Each instance consists of a single sentence, aspect, and corresponding polarity (POSITIVE/NEGATIVE/NEUTRAL).

	```
	{
	"title": "LGU＋ 1분기 영업익 1천706억원…마케팅 비용 감소",
	"aspect": "LG U+",
	'sentiment': 'NEUTRAL',
	'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008363739',
	'annotator_id': 'A_01',
	'Type': 'single'
	}

	```

	### Data Fields

	* title:
	* aspect:
	* sentiment:
	* url:
	* annotator_id:
	* url:


	### Data Splits

	The dataset currently does not contain standard data splits.

	## Additional Information

	You can download the data via:
	```
	from datasets import load_dataset
	dataset = load_dataset("amphora/KorFin-ASC")
	```
	Please find more information about the code and how the data was collected in the paper [Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance](https://arxiv.org/abs/2301.03136).
	The best-performing model on this dataset can be found at [link](https://huggingface.co/amphora/KorFinASC-XLM-RoBERTa).

	### Licensing Information

	KorFin-ASC is licensed under the terms of the [cc-by-sa-4.0](https://creativecommons.org/licenses/by-sa/4.0/)

	### Citation Information

	Please cite this data using:

	```
	@article{son2023removing,
	title={Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance},
	author={Son, Guijin and Lee, Hanwool and Kang, Nahyeon and Hahm, Moonjeong},
	journal={arXiv preprint arXiv:2301.03136},
	year={2023}
	}
	```

	### Contributions

	Thanks to [@Albertmade](https://github.com/h-albert-lee), [@amphora](https://github.com/guijinSON) for making this dataset.