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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Mouaad Errami
Hassan II University of Casablanca
Computer Science & IT Engineering

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Classifying toxicity in the Arabic Moroccan dialect on Instagram: a machine and deep learning approach Rabia Rachidi; Mohamed Amine Ouassil; Mouaad Errami; Bouchaib Cherradi; Soufiane Hamida; Hassan Silkan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp588-598

Abstract

People crave interaction and connection with other people. Therefore, social media became the center of society’s life. Among the brightest social media platforms nowadays with a massive number of daily users there is Instagram, which is due to its distinctive features. The excessive revealing of personal life has put users in the spots of getting bullied and harassed and getting toxic revues from other users. Numerous studies have targeted social media to fight its harmful side effects. Nevertheless, most of the datasets that were already available were in English, the Arabic Moroccan dialect ones were not. In this work, the Arabic Moroccan dialect dataset has been extracted from the Instagram platform. Furthermore, feature extraction techniques have been applied to the collected dataset to increase classification accuracy. Afterward, we developed models using machine learning and deep learning algorithms to detect and classify toxicity. For the models’ evaluation, we have used the most used metrics: accuracy, precision, F1-score, and recall. The experimental results gave modest scores of around 70% to 83%. These results imply that the models need improvement due to the lack of available datasets and the preprocessing libraries to handle the Moroccan dialect of Arabic.
Hybrid convolutional neural network-bidirectional long short-term memory model for Arabic sentence readability assessment Mohamed Amine Ouassil; Mohammed Jebbari; Rabia Rachidi; Mouaad Errami; Soufiane Hamida; Bouchaib Cherradi; Abdelhadi Raihani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2849-2862

Abstract

In the current educational landscape, a large number of educators prefer using generative artificial intelligence techniques to produce textual content to be presented for learning. However, these generated texts may not meet the specific needs of learners or align with their abilities. Many traditional methods and techniques can be employed to assess the complexity of a text, such as traditional readability formulas, but these techniques are time consuming and labor-intensive. In this paper, we introduce a deep learning approach for automatically evaluating the readability of Arabic texts by analyzing and classifying sentences into different difficulty levels within educational content. The initial stage of the proposed approach is preprocessing textual content and leveraging natural language processing (NLP) methodologies for feature extraction, such as Word2Vec. The approach then concentrates on refining and evaluating a deep learning model to classify text into different readability levels. This paper introduces a hybrid classification model that combines convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) layers, attaining an accuracy of 96.68%. This model demonstrates the significance of applying hybrid deep learning models in analyzing educational materials and establishes a foundation for subsequent progress in the field of automated Arabic readability assessment.
Co-Authors Abdelhadi Raihani Bouchaib Cherradi Hassan Silkan Mohamed Amine Ouassil Mohammed Jebbari Rabia Rachidi Soufiane Hamida