Cerist Nlp-challenge2022: Xlm-t For Multilingual Sentiment Analysis In Twitter Using Oversampling Technique

Résumé: 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 2022 for task1.b. The purpose of this task is to identify the sentiment polarity of the CERIST NLP Challenge 2022 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 multilingual in twitter. Experimental results confirm that this state-of-the-art model is robust achieving accuracy of 85%.

Publié dans la revue: Revue de l'Information Scientifique et Technique