%0期刊文章%@ 1438-8871 %I JMIR出版物%V 24%卡塔尔世界杯8强波胆分析 N 10% P e40323% T英国COVID-19大流行期间推特上表达的情绪和主题:比较地理定位与文本挖掘分析%A Alhuzali,Hassan %A Zhang,Tianlin %A Ananiadou,Sophia %+曼彻斯特大学国家文本挖掘中心计算机科学系，曼彻斯特公主街131号，M1 7DN，英国，44 161 306 3092，sophia.ananiadou@manchester.ac.uk %K推特%K COVID-19 %K地理定位%K情感检测%K情感分析%K主题建模%K社交媒体%K自然语言处理%K深度学习%D 2022 %7 5.10.2022 %9原创论文%J J医学互联网Res %G英语%X背景:近年来，COVID-19大流行给公共卫生、社会和经济带来了巨大变化。在疫情期间，社交媒体为人们提供了一个讨论健康问题、生活状况和政策的平台，让决策者可以利用这些内容来分析公众的情绪和态度，以进行决策。目的:本研究的目的是使用基于深度学习的方法，通过Twitter上的比较地理定位和文本挖掘分析，了解英国公众对COVID-19大流行相关主题的情绪。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来地理空间地分析英国推文的情绪、情绪和主题:用于情感分析的SenticNet 6、用于情感识别的SpanEmo和组合主题建模(CTM)。结果:我们观察到，随着流行病学情况和疫苗接种情况在两年内的变化，推文数量发生了显著变化。由于2019冠状病毒病在英国的爆发，从2020年1月到2020年2月，推特的数量急剧增加。到2020年2月，推文数量逐渐下降。 Moreover, with identification of the COVID-19 Omicron variant in the United Kingdom in November 2021, the number of tweets grew again. 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. %M 36150046 %R 10.2196/40323 %U //www.mybigtv.com/2022/10/e40323 %U https://doi.org/10.2196/40323 %U http://www.ncbi.nlm.nih.gov/pubmed/36150046