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

TL;DR

This paper presents a comprehensive global Twitter dataset related to COVID-19, annotated with topics, sentiments, and emotions, enabling multifaceted analysis of public discourse during the pandemic.

Contribution

The creation of a large, annotated COVID-19 Twitter dataset with 17 attributes, combining topic modeling and emotion recognition for diverse research applications.

Findings

Over 252 million tweets collected from 29 million users

Dataset includes detailed sentiment and emotion annotations

Temporal and geographic distribution analyzed

Abstract

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.