Coronavirus disease (COVID-19) is an infectious respiratory disease that was first discovered in late December 2019, in Wuhan, China, and then spread worldwide causing a lot of panic and death. Users of social networking sites such as Facebook and Twitter have been focused on reading, publishing, and sharing novelties, tweets, and articles regarding the
newly emerging pandemic.
A lot of these users often employ sarcasm to convey their intended meaning in a humoristic,funny, and indirect way making it hard for computer-based applications to automatically understand and identify their goal and the harm level that they can convey.
Motivated by the emerging need for annotated datasets that tackle these
kinds of problems in the context of COVID-19, this paper builds and releases AraCOVID19-SSD, a manually annotated Arabic COVID-19 sarcasm and sentiment detection dataset containing 5,162 tweets.
To confirm the practical utility of the built dataset, it has been carefully analyzed and tested using several classification models.
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