Efficient Implementation of Dirty Words Detection in Decision Tree Model

As an informal language, text with dirty words is very common in online comments. Because of their adverse effects on communication users, text analysis with dirty words must be automated. In this paper, we first crawled past reviews that contained dirty words on many networks. Second, we manually marked 20,000 texts to form a corpus containing dirty words. Then use the data to train our model. We use the Decision tree classifier to detect dirty words contained in Malay text. Empirical research shows that our model works well. The F1-score of labels 0 and 1 reached 98% and 93%, and the precision rate of both is over 95%.