Revue d'Information Scientifique et Technique

Modeling Sentiment Analysis Using Machine Learning Algorithms for Arabic covid-19 Tweets

During Covid-19 pandemic period, people worldwide turned to use social media network to express their opinions and general feelings. Social media platforms like Twitter have become widespread tools for broadcasting and distributing
news and opinions. This paper presents our participation to CERIST Natural Language Processing Challenge, task1.c: Arabic sentiment analysis and fake news detection within covid-19. This complex task is further increased when dealing with dialects that do not have the structure of Modern Standard Arabic (MSA). We introduce an experiment of sentiment analysis of Arabic tweets within covid-19 using machine learning algorithms. The used Arabic dataset was provided by the challenge organizers and it contains 4,128 tweets labeled as Positive, Negative and Neutral for training and 1,034 tweets unlabeled for testing Hadj Ameur & Aliane, 2021. In this experiment the opinions are classified by various machine learning classifiers including Support Vector Machine (SVM), Logistic Regression (LR), Multinomial Naïve Bayes (NB) and K-Nearest Neighbors (KNN). The experimental results indicated that the highest accuracy (94%) was obtained using the Logistic-Regression and SVM among other with training times of 8609s.

Auteurs : Yousra F.G.Elhakeem , Safa EltayebMohammed Aldawsari , Omer Salih Dawood Omer


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