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.

原始信息汇总

数据集概述：KorFin-ASC

数据集描述

数据集总结

KorFin-ASC是一个包含8818个样本的数据集，每个样本都标注了(aspect, polarity)对。数据来源于KLUE-TC和Naver Finance的分析师报告。

支持的任务

• 基于方面的情感分类

语言

• 韩语

数据集结构

数据实例

每个实例包含一个句子、一个方面和相应的情感极性（POSITIVE/NEGATIVE/NEUTRAL）。

json { "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" }

数据字段

• title
• aspect
• sentiment
• url
• annotator_id

数据分割

数据集目前不包含标准的数据分割。

许可证信息

KorFin-ASC根据cc-by-sa-4.0许可证授权。

引用信息

@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} }