Datasets:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ko
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Korean Sarcasm Detection
tags:
- sarcasm-detection
dataset_info:
features:
- name: tokens
dtype: string
- name: label
dtype:
class_label:
names:
'0': no_sarcasm
'1': sarcasm
splits:
- name: train
num_bytes: 1012030
num_examples: 9000
- name: test
num_bytes: 32480
num_examples: 301
download_size: 1008955
dataset_size: 1044510
Dataset Card for Korean Sarcasm Detection
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: Korean Sarcasm Detection
- Repository: Korean Sarcasm Detection
- Point of Contact: Dionne Kim
Dataset Summary
The Korean Sarcasm Dataset was created to detect sarcasm in text, which can significantly alter the original meaning of a sentence. 9319 tweets were collected from Twitter and labeled for sarcasm
or not_sarcasm
. These tweets were gathered by querying for: 역설, 아무말, 운수좋은날, 笑, 뭐래 아닙니다, 그럴리없다, 어그로, irony sarcastic, and sarcasm
. The dataset was pre-processed by removing the keyword hashtag, urls and mentions of the user to maintain anonymity.
Supported Tasks and Leaderboards
sarcasm_detection
: The dataset can be used to train a model to detect sarcastic tweets. A BERT model can be presented with a tweet in Korean and be asked to determine whether it is sarcastic or not.
Languages
The text in the dataset is in Korean and the associated is BCP-47 code is ko-KR
.
Dataset Structure
Data Instances
An example data instance contains a Korean tweet and a label whether it is sarcastic or not. 1
maps to sarcasm and 0
maps to no sarcasm.
{
"tokens": "[ 수도권 노선 아이템 ] 17 . 신분당선의 #딸기 : 그의 이미지 컬러 혹은 머리 색에서 유래한 아이템이다 . #메트로라이프"
"label": 0
}
Data Fields
tokens
: contains the text of the tweetlabel
: determines whether the text is sarcastic (1
: sarcasm,0
: no sarcasm)
Data Splits
The data is split into a training set comrpised of 9018 tweets and a test set of 301 tweets.
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
The dataset was created by gathering HTML data from Twitter. Queries for hashtags that include sarcasm and variants of it were used to return tweets. It was preprocessed by removing the keyword hashtag, urls and mentions of the user to preserve anonymity.
Who are the source language producers?
The source language producers are Korean Twitter users.
Annotations
Annotation process
Tweets were labeled 1
for sarcasm and 0
for no sarcasm.
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
Mentions of the user in a tweet were removed to keep them anonymous.
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
This dataset was curated by Dionne Kim.
Licensing Information
This dataset is licensed under the MIT License.
Citation Information
@misc{kim2019kocasm,
author = {Kim, Jiwon and Cho, Won Ik},
title = {Kocasm: Korean Automatic Sarcasm Detection},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/SpellOnYou/korean-sarcasm}}
}
Contributions
Thanks to @stevhliu for adding this dataset.