@文章{信息:doi/10.2196/40323，作者=“Alhuzali, Hassan and Zhang, Tianlin and Ananiadou, Sophia”，标题=“英国COVID-19大流行期间推特上表达的情感和话题:比较地理定位和文本挖掘分析”，期刊=“J Med Internet Res”，年=“2022”，月=“10”，日=“5”，卷=“24”，数=“10”，页=“e40323”，关键词=“推特;COVID-19;地理位置;情感检测;情绪分析;主题建模;社交媒体;自然语言处理;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。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。 Objective: The aim of this study was to use deep learning--based methods to understand public emotions on topics related to the COVID-19 pandemic in the United Kingdom through a comparative geolocation and text mining analysis on Twitter. Methods: Over 500,000 tweets related to COVID-19 from 48 different cities in the United Kingdom were extracted, with the data covering the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning--based models for topic modeling to geospatially analyze the sentiment, emotion, and topics of tweets in the United Kingdom: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and combined topic modeling (CTM). Results: We observed a significant change in the number of tweets as the epidemiological situation and vaccination situation shifted over the 2 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 United Kingdom. Then, the number of tweets gradually declined as of February 2020. 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. ", issn="1438-8871", doi="10.2196/40323", url="//www.mybigtv.com/2022/10/e40323", url="https://doi.org/10.2196/40323", url="http://www.ncbi.nlm.nih.gov/pubmed/36150046" }