OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach
This work addresses the challenge of classifying informal Arabic text on social media, which is incremental as it focuses on preprocessing improvements for existing tasks.
The study tackled offensive language and hate speech detection in Arabic social media text by applying intensive preprocessing techniques, achieving state-of-the-art performance with F1 scores of 89% for offensive language detection and 95% for hate speech detection.
The preprocessing phase is one of the key phases within the text classification pipeline. This study aims at investigating the impact of the preprocessing phase on text classification, specifically on offensive language and hate speech classification for Arabic text. The Arabic language used in social media is informal and written using Arabic dialects, which makes the text classification task very complex. Preprocessing helps in dimensionality reduction and removing useless content. We apply intensive preprocessing techniques to the dataset before processing it further and feeding it into the classification model. An intensive preprocessing-based approach demonstrates its significant impact on offensive language detection and hate speech detection shared tasks of the fourth workshop on Open-Source Arabic Corpora and Corpora Processing Tools (OSACT). Our team wins the third place (3rd) in the Sub-Task A Offensive Language Detection division and wins the first place (1st) in the Sub-Task B Hate Speech Detection division, with an F1 score of 89% and 95%, respectively, by providing the state-of-the-art performance in terms of F1, accuracy, recall, and precision for Arabic hate speech detection.