Sentiment Analysis of Arabic YouTube Comments - Bani Issa, Esra'a;Al-Tamimi, Abdel-Karim;Shatnawi, Ali
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With the current level of ubiquity of social media websites, obtaining online users preferences automatically became a crucial task to assess their tendencies and behaviors online. Arabic language is one of the most spoken languages in the world, and the fastest growing language on the Internet which motivates us to provide automated tools for Arabic language that can perform reliable sentiment analysis to deduct users opinions.In this book, we present our work of Arabic comments sentiments classification based on our collected and manually annotated corpora of YouTube Arabic comments. We…mehr

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Produktbeschreibung
With the current level of ubiquity of social media websites, obtaining online users preferences automatically became a crucial task to assess their tendencies and behaviors online. Arabic language is one of the most spoken languages in the world, and the fastest growing language on the Internet which motivates us to provide automated tools for Arabic language that can perform reliable sentiment analysis to deduct users opinions.In this book, we present our work of Arabic comments sentiments classification based on our collected and manually annotated corpora of YouTube Arabic comments. We share our classification results utilizing several machine learning classifiers: SVM-RBF, linear SVM OAO, linear SVM OAA, Perceptron, Passive Aggressive, SGD, Random Forest, Logistic Regression, Bernoulli NB and KNN.
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Autorenporträt
Esra'a received her M.Sc. degree in Computer Engineering from Jordan University of Science and Technology (JUST) in 2018. Her research interest includes machine learning, deep learning, text analysis, and artificial intelligence (AI).