온라인 커뮤니티에서 사용되는 댓글의 형태를 고려한 악플 탐지를 위한 전처리 기법

Abstract

With the spread of the Internet, anonymous communities emerged along with the activation of communities for communication betweenpeople, and many users are doing harm to others, such as posting aggressive posts and leaving comments using anonymity. In the past,administrators directly checked posts and comments, then deleted and blocked them, but as the number of community users increased,they reached a level that managers could not continue to monitor. Initially, word filtering techniques were used to prevent maliciouswriting from being posted in a form that could not post or comment if a specific word was included, but they avoided filtering in abypassed form, such as using similar words. As a way to solve this problem, deep learning was used to monitor posts posted by usersin real-time, but recently, the community uses words that can only be understood by the community or from a human perspective, notfrom a general Korean word. There are various types and forms of characters, making it difficult to learn everything in the artificialintelligence model. Therefore, in this paper, we proposes a preprocessing technique in which each character of a sentence is imagedusing a CNN model that learns the consonants, vowel and spacing images of Korean word and converts characters that can only beunderstood from a human perspective into characters predicted by the CNN model. As a result of the experiment, it was confirmedthat the performance of the LSTM, BiLSTM and CNN-BiLSTM models increased by 3.2%, 3.3%, and 4.88%, respectively, through the proposedpreprocessing technique.