Extracting Feelings of People Regarding COVID-19 by Social Network Mining
This work provides insights into public opinion during the COVID-19 pandemic for policymakers and researchers, but it is incremental as it applies existing methods to new data.
The researchers tackled the problem of understanding public sentiment about COVID-19 by analyzing over two million English tweets from March to June 2020, using RoBERTa for sentiment analysis and GeoNames for location tagging, and found that tweet frequencies for most nations correlated significantly with official daily case statistics.
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.