  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.
