An Intelligent COVID-19-Related Arabic Text Detection Framework Based on Transfer Learning Using Context Representation
Abdullah Y. Muaad, Shaina Raza, Md Belal Bin Heyat, Amerah Alabrah, J. Hanumanthappa
Abstract
The misleading information during the coronavirus disease 2019 (COVID-19) pandemic’s peak time is very sensitive and harmful in our community. Analyzing and detecting COVID-19 information on social media are a crucial task. Early detection of COVID-19 information is very helpful and minimizes the risk of psychological security which leads to inconvenience in daily life. In this paper, a deep ensemble transfer learning framework with an understanding of the context of Arabic text COVID-19 information is proposed. This framework is inspired to spontaneously analyze and recognize the text about COVID-19. The ArCOVID-19Vac dataset has been used to train and test our proposed model. A comprehensive experimental study for each scenario is performed. For the binary classification scenario, the proposed framework records better evaluation results with 83.0%, 84.0%, 83.0%, and 84.0% in terms of accuracy, precision, recall, and F 1-score, respectively. For the second scenario (three classes), the overall performance is recorded with an accuracy of 82.0%, precision of 80.0%, recall of 82.0%, and F 1-score of 80.0%, respectively. In the last scenario with ten classes, the best evaluation performance results are recorded with an accuracy of 67.0%, a precision of 58.0%, a recall of 67.0%, and F 1-score of 59.0%, respectively. In addition, we have applied an ensemble transfer learning model for this scenario to get 64.0%, 66.0%, 66.0%, and 65.0% in terms of accuracy, precision, recall, and F 1-score, respectively. The results show that the proposed model through transfer learning provides better results for Arabic text than all state-of-the-art methods.