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المؤلفHamad, Omama
المؤلفHamdi, Ali
المؤلفHamdi, Sayed
المؤلفShaban, Khaled
تاريخ الإتاحة2022-12-21T10:01:48Z
تاريخ النشر2022
اسم المنشورBig Data and Cognitive Computing
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.3390/bdcc6030088
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37527
الملخصIn this paper, we present StEduCov, an annotated dataset for the analysis of stances toward online education during the COVID-19 pandemic. StEduCov consists of 16,572 tweets gathered over 15 months, from March 2020 to May 2021, using the Twitter API. The tweets were manually annotated into the classes agree, disagreeor neutral. We performed benchmarking on the dataset using state-of-the-art and traditional machine learning models. Specifically, we trained deep learning models-bidirectional encoder representations from transformers, long short-term memory, convolutional neural networks, attention-based biLSTM and Naive Bayes SVM-in addition to naive Bayes, logistic regression, support vector machines, decision trees, K-nearest neighbor and random forest. The average accuracy in the 10-fold cross-validation of these models ranged from 75% to (Formula presented.) % and from (Formula presented.) % to 68% for binary and multi-class stance classifications, respectively. Performances were affected by high vocabulary overlaps between classes and unreliable transfer learning using deep models pre-trained on general texts in relation to specific domains such as COVID-19 and distance education. 2022 by the authors.
اللغةen
الناشرMDPI
الموضوعCOVID-19 pandemic
deep learning
stance detection
text classification
transfer learning
العنوانStEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic
النوعArticle
رقم العدد3
رقم المجلد6
dc.accessType Open Access


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