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

arXiv: https://arxiv.org/abs/2006.12185 · HTML: https://mlsanity.com/paper/2006.12185/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

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

- [COVID-19 sentiment analysis via deep learning during the rise of novel cases](https://mlsanity.com/paper/2104.10662/) (2021)
- [Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox](https://mlsanity.com/paper/2312.10580/) (2023)
- [Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions](https://mlsanity.com/paper/2111.04415/) (2021)
- [An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines](https://mlsanity.com/paper/2306.13797/) (2023)
- [BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets](https://mlsanity.com/paper/2211.09733/) (2022)

## Abstract (arXiv)

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 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 five 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.