Study on the N-gram measure based flame detection in Korean online messages

Abstract

People often use the internet in order to express their opinions for specific issues or to get some information. Flames among online messages disrupt those uses. In this paper, I propose a heuristic method which detects flames from online messages automatically using an n-gram language model. We focus on flaming in Korean web sites, but our system can be applied to any other languages. I propose a method to extract features based on n-grams and score each feature by a heuristic method. The proposed algorithm outperforms a wordbased algorithm in terms of the accuracy and the recall rates, because the algorithm presented in this paper can solve the two problems: variants of words and abbreviations of blanks. In the evaluation, I compare the proposed method with the word-based algorithm and the algorithm based on an n-gram language model which use SVM learning machine. While the proposed algorithm does not need any stemming and tagging tasks, it can detect more accurately by 10% than the algorithm based on words.