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• W4386985395 abstract "We performed an analysis of tweets concerning the COVID-19 pandemic in the UK over a two-year period, selecting fifteen timelines. Over 110,000 tweets were obtained from Twitter and analysed using BERT and Text2Emotions for sentiment and emotion analysis, respectively. The most common emotions expressed on Twitter about COVID-19 in the UK appeared to be surprise and fear. This is not unusual, given the unprecedented nature of the pandemic. However, as time passed, there was a notable shift in sentiment towards other emotions, such as sad- ness and happiness. Moreover, more positive than negative sentiments were observed over the fifteen timelines studied: eight positive sentiments to seven negative ones. Further, results indicated that confirmed cases, deaths, and government policy heavily influenced public sentiment. This study sheds light on the collective state of mind surrounding the pandemic and provides insight into how people reacted emotionally over time to COVID-19. The results provide valuable insights for policymakers and other stakeholders looking to understand how people respond in times of crisis. Furthermore, it illustrates how sentiment analysis can be used effectively to gain deeper insights into public perception over time. As such, this study is a valuable contribution to understanding the human emotional response, demonstrating how sentiment and emotion can be used to better comprehend a situation and react accordingly." @default.
• W4386985395 created "2023-09-24" @default.
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• W4386985395 date "2023-01-01" @default.
• W4386985395 modified "2023-10-18" @default.
• W4386985395 title "COVID-19 in the UK: Sentiment and Emotion Analysis of Tweets Over Time" @default.
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• W4386985395 doi "https://doi.org/10.1007/978-3-031-37164-6_38" @default.
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