Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from a State in Australia

Abstract

The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused unprecedented impacts to people’s daily life around the world. Various measures and policies such as lockdown and social-distancing are implemented by governments to combat the disease during the pandemic period. These measures and policies as well as virus itself may cause different mental health issues to people such as depression, anxiety, sadness, etc. In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period. Different from the existing work that mostly focuses on the country-level and static sentiment analysis, we analyse the sentiment dynamics at the fine-grained local government areas (LGAs). Based on the analysis of around 94 million tweets that posted by around 183 thousand users located at different LGAs in NSW in 5 months, we found that people in NSW showed an overall positive sentimental polarity and the COVID-19 pandemic decreased the overall positive sentimental polarity during the pandemic period. The fine-grained analysis of sentiment in LGAs found that despite the dominant positive sentiment most of days during the study period, some LGAs experienced significant sentiment changes from positive to negative. This study also analysed the sentimental dynamics delivered by the hot topics in Twitter such as government policies (e.g. the Australia’s JobKeeper program, lockdown, social-distancing) as well as the focused social events (e.g. the Ruby Princess Cruise). The results showed that the policies and events did affect people’s overall sentiment, and they affected people’s overall sentiment differently at different stages.

Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media

“…They also discovered that "fake news" sites are most likely to be retweeted within the source country and so are less likely to spread internationally. Zhou et al [24] exploited tweets on Twitter to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period. They first summarized that the overall polarity of the community since the outbreak was positive, and then analyzed the sentiment dynamics of the NSW local government areas in terms of lockdown, social distance and JobKeeper.…”

“…The above measures have changed people's daily lives, and most people have to follow the policies to protect the safety of their communities. However, many mental symptoms like worry, fear, frustration, depression and anxiety could occur and cause serious mental heath issues to people due to the long-time social activity restriction during the pandemic period [24]. Therefore, understanding when and where people would experience mental issues in this special period is important for governments to make the right decisions.…”

“…Such massive personal posts from social media could become invaluable data sources for large-scale sentiment and topic mining for monitoring people's mental health across different events or topics [21]. Hence, many recent work focuses to detect the social sentiment for people due to COVID-19 [24,10,15]. For instance, Zhou et al [24] adopted Twitter data for massive sentiment analysis due to COVID-19 for people living New South Wales Australia.…”

“…Hence, many recent work focuses to detect the social sentiment for people due to COVID-19 [24,10,15]. For instance, Zhou et al [24] adopted Twitter data for massive sentiment analysis due to COVID-19 for people living New South Wales Australia. Han et al [10] studied the public opinion in the early stages of COVID-19 in China by analyzing text data from Sina Weibo.…”

Sentiment analysis and topic modeling for COVID-19 vaccine discussions

“…2 According to the latest news from the WHO, as of August 23, 2021, more than 200 million people have been infected, and over 4.42 million people have died of the COVID-19 3 . It is considered one of the most severe epidemics of this century, comparable to past pandemics such as the Spanish flu in 1918 and the Black Death in the mid-13th century [ 28 ]. Eliminating COVID-19 has become a common goal of the world.…”