Unsupervised Text Mining of COVID-19 Records
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Abstract (arXiv)
Since the beginning of coronavirus, the disease has spread worldwide and drastically changed many aspects of the human's lifestyle. Twitter as a powerful tool can help researchers measure public health in response to COVID-19. According to the high volume of data production on social networks, automated text mining approaches can help search, read and summarize helpful information. This paper preprocessed the existing medical dataset regarding COVID-19 named CORD-19 and annotated the dataset for supervised classification tasks. At this time of the COVID-19 pandemic, we made a preprocessed dataset for the research community. This may contribute towards finding new solutions for some social interventions that COVID-19 has made. The preprocessed version of the mentioned dataset is publicly available through Github.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 23 references that are in the map. First posted on arXiv 8 Sep 2021.
Builds on
4 of 23 references.
References · 4
- 2020CORD-19: The COVID-19 Open Research Datasetcited by 179
- 2020A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaborationcited by 33
- 2020MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resourcescited by 8
- 2020Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executivescited by 7
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