In the last decade, social media and internet involvement in people’s life raised new challenges that modern AI needs to deal with. Textual data is generated every time an article is published or an online post is shared or even a simple
comment is made. Among these challenges, we find text classification which is used to identify the general meaning of a set of words using AI methods. This paper presents our participation to the CERIST Natural Language Processing
Challenge, where we proposed a simple yet effective convolutional neural network architecture that can be used for text classification and sentiment analysis. We tested our proposition on 5 different tweets datasets, Hate Speech, Fake News, Arabic Covid Sentiment, Arabic Sentiment, and English Sentiment, and obtained respectively 99,85%, 99,86%, 99,58%, 97,97%, 95,65% accuracy on the training subset and 98,43%, 94,74%, 87,53%, 54,90%, 60,62% accuracy on the validation subset.

Auteurs : Zoubir TALAI , Nada KHERICI

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