하이웨이 네트워크 기반 CNN 모델링 및 사전 외 어휘 처리 기술을 활용한 악성 댓글 분류 연구
A Study on the Toxic Comments Classification Using CNN Modeling with Highway Network and OOV Process
- 발행
- 2020 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
- 경북대학교
- 출처
- 국내 KCI
- 원문
- 원문 보기 ↗
개념
키워드
deep learning, Highway Network, CNN, OOV, toxic comments, deep learning, Highway Network, CNN, OOV, toxic comments
초록
Purpose: Recently, various issues related to toxic comments on web portal sites and SNS are becoming a major social problem. Toxic comments can threaten Internet users in the type of defamation, personal attacks, and invasion of privacy. Over past few years, academia and industry have been conducting research in various ways to solve this problem. The purpose of this study is to develop the deep learning modeling for toxic comments classification.
 Design/methodology/approach: This study analyzed 7,878 internet news comments through CNN classification modeling based on Highway Network and OOV process.
 Findings: The bias and hate expressions of toxic comments were classified into three classes, and achieved 67.49% of the weighted f1 score. In terms of weighted f1 score performance level, this was superior to approximate 50~60% of the previous studies.