Extracting Feelings of People Regarding COVID-19 by Social Network Mining

TL;DR

This study analyzes over two million COVID-19 related tweets from early 2020 to extract public feelings, using geographic labeling and advanced sentiment analysis, revealing correlations with official infection statistics.

Contribution

It introduces a combined geolocation and RoBERTa-based sentiment analysis method for large-scale social media data related to COVID-19.

Findings

Sentiment trends correlate with official infection data.

Geolocation labeling enhances sentiment analysis accuracy.

Implicit knowledge about public opinion during early pandemic stages.

Abstract

In 2020, COVID-19 became the chief concern of the world and is still reflected widely in all social networks. Each day, users post millions of tweets and comments on this subject, which contain significant implicit information about the public opinion. In this regard, a dataset of COVID-related tweets in English language is collected, which consists of more than two million tweets from March 23 to June 23 of 2020 to extract the feelings of the people in various countries in the early stages of this outbreak. To this end, first, we use a lexicon-based approach in conjunction with the GeoNames geographic database to label the tweets with their locations. Next, a method based on the recently introduced and widely cited RoBERTa model is proposed to analyze their sentimental content. After that, the trend graphs of the frequency of tweets as well as sentiments are produced for the world and the nations that were more engaged with COVID-19. Graph analysis shows that the frequency graphs of the tweets for the majority of nations are significantly correlated with the official statistics of the daily afflicted in them. Moreover, several implicit knowledge is extracted and discussed.