A comparative geolocation and text mining analysis of emotions and topics during the COVID-19 Pandemic in the UK.

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Abstract

In recent years, the COVID-19 pandemic has brought great changes to public health, society and the economy. Social media provides a platform for people to discuss health concerns, living conditions and policies during the epidemic, which allows policy makers to use its contents to analyse the public emotions and attitudes for decision making.In this study, we aim to use deep learning-based methods to understand public emotions on topics related to the COVID-19 pandemic in the UK through a comparative geolocation and text mining analysis on Twitter.Over 500,000 tweets related to COVID-19 from 48 different cities in the UK were extracted, and the data cover the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning-based models: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and Combined Topic Modelling (CTM) for topic modelling to geospatially analyse the sentiment, emotion and topics of tweets in the UK.According to the analysis, we observed a significant change in the number of tweets as the epidemiological situation and vaccination these two years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the UK. Then, the number of tweets gradually declined from February 2020. Moreover, with the identification of the COVID-19 Omicron variant in the UK in November 2021, the number of tweets grew. Our findings reveal people’s attitudes and emotions towards topics related to COVID-19. For sentiment, about 60% of tweets are positive, 20% neutral and 20% are negative. For emotion, people tend to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions as the time changes towards the end of 2021. The topics are also changing during the pandemic.Through large scale text mining of Twitter, our study found that there were meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people’s thoughts, feelings and understand their behaviours. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak epidemiological change, the government developed control measures, vaccination policies and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.