Natural Language Processing has recently become one of the most trending research areas in Artificial Intelligence, especially in social media-related tasks. This paper describes our participation in the « Hate Speech Detection on Arabic Twitter” task at the CERIST NLP-Challenge 2022 competition. The proposed solution aims to classify the tweets collected in the Arabic ARACOVID19-MFH multi-label and multi-dialect dataset into « Hateful » and « Not Hateful » categories. Based on a pre-trained transformer model known as GigaBERT-v4, our solution outperformed the most common transformer models supporting the Arabic language. Experiments have proved that the GigaBERT-v4 model is more effective than the other models using the previously described dataset, obtaining a 99.46% accuracy and a 98.68% macro F1-score.

Auteurs : Bachir Said  , Mohammed E. Barmati

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With the emergence of Pre-trained Language Models (PLMs) and the success of large scale, the field of Natural Language Processing (NLP) has achieved tremendous development such as Sentiment analysis (SA) that is one of the fast-growing research tasks in NLP. This paper describes the system that our team submitted to the CERIST NLP Challenge, for task 1.b. The purpose of this task is to identify the sentiment polarity of the datasets in English and Arabic languages comments collected from twitter. Our approach is based on a PL Model called XLM-T, and uses the Oversampling technique to solve the sentiment analysis problem of multilingualism in twitter. Experimental results confirm that this state-of-the-art model is robust achieving accuracy of 85%.

Auteurs :  Mohammed E. Barmati , Bachir Said

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