악성 댓글에 대한 한국어 혐오표현 및 편견 탐지 분류 모형 결과 분석 및 개선방안 연구
Analyzing the Classification Results for Korean Hatespeech and Bias Detection Models in Malicious Comment Dataset
- 발행
- 2022 등록이 논문은 발행 시점을 확인하지 못해 원문 등록일을 적었습니다. KCI가 2005~2008년에 옛 논문을 몰아서 올린 탓에, 그 시기 등록분은 발행보다 평균 3~5년 늦습니다. 실제 발행연도는 더 이를 수 있습니다.
- 소속·발행
- 성신여자대학교
- 출처
- 국내 KCI
- DOI
- 10.7232/JKIIE.2022.48.6.636
- 원문
- 원문 보기 ↗
개념
키워드
Korean Hatespeech Classification, Bias Classification, Malicious Comments, Korean Hatespeech Classification, Bias Classification, Malicious Comments
초록
With the development of Internet communication technology, opinions on various issues can be freely expressed on the Internet. However, some people have abused their freedom of expression, causing psychological harm by writing comments expressing their hatred towards others. In order to address this problem, research on automatic detection of malicious comments using machine learning models has been actively conducted. In this study, we constructed the detection models for hate speech and bias to classify KOCO (KOrean hate COmments) dataset using popular language classification models such as logistic regression with term frequency-inverse document frequency, KoBERT, KoELECTRA, KcELECTRA and KoGPT2 models. Through the experiments, we demonstrated that sentence length, reflection of context information, and mis-labeled data highly affected the classification performance of most models. As a result, we presented considerations for automatic detection of malicious comments and directions for constructing the comment dataset to improve the detection models in future research.