# COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes

arXiv: https://arxiv.org/abs/2007.06954 · HTML: https://mlsanity.com/paper/2007.06954/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter](https://mlsanity.com/paper/2304.06858/) (2023)
- [A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets](https://mlsanity.com/paper/2111.08658/) (2021)
- [A Multimodal Framework for Depression Detection during Covid-19 via Harvesting Social Media: A Novel Dataset and Method](https://mlsanity.com/paper/2511.00424/) (2025)
- [How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and India](https://mlsanity.com/paper/2303.10560/) (2023)
- [Rumour Detection and Analysis on Twitter](https://mlsanity.com/paper/2304.01712/) (2023)

## Abstract (arXiv)

This paper describes a large global dataset on people's discourse and responses to the COVID-19 pandemic over the Twitter platform. From 28 January 2020 to 1 June 2022, we collected and processed over 252 million Twitter posts from more than 29 million unique users using four keywords: "corona", "wuhan", "nCov" and "covid". Leveraging probabilistic topic modelling and pre-trained machine learning-based emotion recognition algorithms, we labelled each tweet with seventeen attributes, including a) ten binary attributes indicating the tweet's relevance (1) or irrelevance (0) to the top ten detected topics, b) five quantitative emotion attributes indicating the degree of intensity of the valence or sentiment (from 0: extremely negative to 1: extremely positive) and the degree of intensity of fear, anger, sadness and happiness emotions (from 0: not at all to 1: extremely intense), and c) two categorical attributes indicating the sentiment (very negative, negative, neutral or mixed, positive, very positive) and the dominant emotion (fear, anger, sadness, happiness, no specific emotion) the tweet is mainly expressing. We discuss the technical validity and report the descriptive statistics of these attributes, their temporal distribution, and geographic representation. The paper concludes with a discussion of the dataset's usage in communication, psychology, public health, economics, and epidemiology.