MOTEC: The Malay Offensive Text Classification using Extra Tree and Language Standardization - Amalina, Fairuz; Zaki, Faiz; Altarturi, Hamza H. M.; Hanif, Hazim; Anuar, Nor Badrul (Malaysian Journal of Computer Science, 2025)
Cyberbullying has increased globally, with offensive text contributing significantly. Detecting offensive text in Malay is challenging due to non-standard Malay text, unique social media writing styles, a lack of standardization, and limited resources. This study proposes the Malay Offensive Text Classification (MOTEC) framework to address these challenges. The MOTEC framework incorporates a Malay standardization preprocessing task, utilizing three specialized dictionaries: (a) abbreviations, (b) noisy text, and (c) Malaysian dialects. This approach enhances data quality by converting non-standard text into standardized Malay sentences before classification. For feature extraction, the framework employs Term Frequency-Inverse Document Frequency (TF-IDF). This statistical method evaluates the importance of words in a document relative to a collection of documents, coupled with an Extra Tree classifier for the classification process. Evaluating the MOTEC framework using a private dataset collected from Twitter, this study achieved a classification accuracy of 94%, significantly outperforming other studies, which reported an accuracy of 84%. The MOTEC framework substantially improves the classification of offensive Malay text by enhancing accuracy, reducing execution time, and improving data quality through effective language standardization.