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Title |
Arabic Sentiment Analysis using Deep Learning for COVID-19 Twitter Data |
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Author |
Sarah Alhumoudi |
| Citation |
Vol. 20 No. 9 pp. 132-138 |
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Abstract |
Novel coronavirus, (COVID-19) first noticed in December 2019, and became a world pandemic affecting not only the health sector, but economic, social and psychological wellbeing as well. Individuals are using social media platforms to communicate feelings and sentiments on this pandemic. This article aims at analyzing and visualizing the influence of coronavirus (COVID-19) using machine learning and deep learning methods to quantify the sentiment shared publicly corelated with the actual number of cases reported over time. On the analysis of 10 Million Arabic tweets, results show that deep learning techniques using an ensemble model outperformed machine learning using SVM with an accuracy of 90% and 77% respectively. It also outperformed the individual deep learning models. |
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Keywords |
COVID-19, machine learning, sentiment analysis, social computing. |
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