# TweetCOVID: A System for Analyzing Public Sentiments and Discussions about COVID-19 via Twitter Activities

arXiv: https://arxiv.org/abs/2103.01472 · HTML: https://mlsanity.com/paper/2103.01472/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [Real-time Spatio-temporal Event Detection on Geotagged Social Media](https://mlsanity.com/paper/2106.13121/) (2021)
- [Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling](https://mlsanity.com/paper/2112.12940/) (2021)
- [User Identification across Social Networking Sites using User Profiles and Posting Patterns](https://mlsanity.com/paper/2106.11815/) (2021)

## Abstract (arXiv)

The COVID-19 pandemic has created widespread health and economical impacts, affecting millions around the world. To better understand these impacts, we present the TweetCOVID system that offers the capability to understand the public reactions to the COVID-19 pandemic in terms of their sentiments, emotions, topics of interest and controversial discussions, over a range of time periods and locations, using public tweets. We also present three example use cases that illustrates the usefulness of our proposed TweetCOVID system.