AraCOVID19-SSD: Arabic COVID-19 Sentiment and Sarcasm Detection Dataset

TL;DR

This paper introduces AraCOVID19-SSD, a new Arabic dataset of 5,162 COVID-19 related tweets annotated for sentiment and sarcasm, and demonstrates its usefulness through classification experiments.

Contribution

The paper presents the first manually annotated Arabic COVID-19 sarcasm and sentiment dataset and evaluates its effectiveness with classification models.

Findings

The dataset effectively supports sentiment and sarcasm detection tasks.

Classification models achieved promising results on the dataset.

The dataset facilitates research in Arabic COVID-19 social media analysis.

Abstract

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 humorous, 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 inflect. 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.