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TIIS (한국인터넷정보학회)

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한글제목(Korean Title) Profane or Not: Improving Korean Profane Detection using Deep Learning
영문제목(English Title) Profane or Not: Improving Korean Profane Detection using Deep Learning
저자(Author) Jiyoung Woo   Sung Hee Park   Huy Kang Kim  
원문수록처(Citation) VOL 16 NO. 01 PP. 0305 ~ 0318 (2022. 01)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
Abusive behaviors have become a common issue in many online social media platforms. Profanity is common form of abusive behavior in online. Social media platforms operate the filtering system using popular profanity words lists, but this method has drawbacks that it can be bypassed using an altered form and it can detect normal sentences as profanity. Especially in Korean language, the syllable is composed of graphemes and words are composed of multiple syllables, it can be decomposed into graphemes without impairing the transmission of meaning, and the form of a profane word can be seen as a different meaning in a sentence. This work focuses on the problem of filtering system mis-detecting normal phrases with profane phrases. For that, we proposed the deep learning-based framework including grapheme and syllable separation-based word embedding and appropriate CNN structure. The proposed model was evaluated on the chatting contents from the one of the famous online games in South Korea and generated 90.4% accuracy.
키워드(Keyword) Profanity   deep learning   convolutional neural network   text mining   natural language processing  
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