# Unsupervised Text Mining of COVID-19 Records

arXiv: https://arxiv.org/abs/2110.07357 · HTML: https://mlsanity.com/paper/2110.07357/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration](https://mlsanity.com/paper/2004.03688/) (2020)
- [MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources](https://mlsanity.com/paper/2005.12443/) (2020)
- [Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives](https://mlsanity.com/paper/2004.11692/) (2020)

## 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.