TY -的盟Alhuzali哈桑盟——张Tianlin盟——Ananiadou索菲亚PY - 2022 DA - 2022/10/5 TI -情绪和主题在Twitter上表示COVID-19大流行期间在英国:比较地理位置和文本挖掘分析乔- J地中海互联网Res SP - e40323六世- 24 - 10 KW - Twitter KW - COVID-19 KW -地理位置KW -情感检测KW -情绪分析KW -主题建模KW -社会媒体KW -自然语言处理KW -深度学习AB -背景:近年来，新冠肺炎大流行给公共卫生、社会和经济带来了巨大变化。社交媒体为人们在疫情期间讨论健康问题、生活状况和政策提供了平台，决策者可以利用这些内容分析公众的情绪和态度，以便做出决策。目的:本研究旨在使用基于深度学习的方法，通过对比地理定位和文本挖掘分析，了解公众对英国COVID-19大流行相关话题的情绪。方法:提取了来自英国48个不同城市的50多万条与COVID-19有关的推文，数据涵盖了过去两年(2020年2月至2021年11月)。我们利用三种先进的基于深度学习的主题建模模型对英国推文的情感、情绪和主题进行地理空间分析:SenticNet 6用于情感分析，SpanEmo用于情感识别，以及组合主题建模(CTM)。结果:我们观察到，随着流行病学情况和疫苗接种情况的变化，推文数量发生了显著变化。由于英国COVID-19的爆发，从2020年1月到2020年2月，推特的数量急剧增加。然后，到2020年2月，推文的数量逐渐下降。此外，随着2021年11月在英国发现COVID-19变体Omicron，推文的数量再次增长。 Our findings reveal people’s attitudes and emotions toward topics related to COVID-19. For sentiment, approximately 60% of tweets were positive, 20% were neutral, and 20% were negative. For emotion, people tended to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions over time toward the end of 2021. The topics also changed during the pandemic. Conclusions: Through large-scale text mining of Twitter, our study found 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 and feelings, and to understand their behaviors. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak and epidemiological change, the government developed control measures and 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. SN - 1438-8871 UR - //www.mybigtv.com/2022/10/e40323 UR - https://doi.org/10.2196/40323 UR - http://www.ncbi.nlm.nih.gov/pubmed/36150046 DO - 10.2196/40323 ID - info:doi/10.2196/40323 ER -