Arabic Offensive Language Detection Using Machine Learning and Ensemble Machine Learning Approaches
This work addresses the problem of detecting offensive language in Arabic, which is challenging due to dialects and informality, but it is incremental as it applies known ensemble methods to this domain.
The study tackled offensive language detection in Arabic social media text by comparing single learner and ensemble machine learning approaches, finding that bagging achieved an F1 score of 88%, outperforming the best single learner by 6%.
This study aims at investigating the effect of applying single learner machine learning approach and ensemble machine learning approach for offensive language detection on Arabic language. Classifying Arabic social media text is a very challenging task due to the ambiguity and informality of the written format of the text. Arabic language has multiple dialects with diverse vocabularies and structures, which increase the complexity of obtaining high classification performance. Our study shows significant impact for applying ensemble machine learning approach over the single learner machine learning approach. Among the trained ensemble machine learning classifiers, bagging performs the best in offensive language detection with F1 score of 88%, which exceeds the score obtained by the best single learner classifier by 6%. Our findings highlight the great opportunities of investing more efforts in promoting the ensemble machine learning approach solutions for offensive language detection models.