The outbreak of the novel Coronavirus Disease (COVID-19) has greatly influenced people's daily lives across the globe. Emergent measures and policies (e.g., lockdown, social distancing) have been taken by governments to combat this highly infectious disease. However, people's mental health is also at risk due to the long-time strict social isolation rules. Hence, monitoring people's mental health across various events and topics will be extremely necessary for policy makers to make the appropriate decisions. On the other hand, social media have been widely used as an outlet for people to publish and share their personal opinions and feelings. The large scale social media posts (e.g., tweets) provide an ideal data source to infer the mental health for people during this pandemic period. In this work, we propose a novel framework to analyze the topic and sentiment dynamics due to COVID-19 from the massive social media posts. Based on a collection of 13 million tweets related to COVID-19 over two weeks, we found that the positive sentiment shows higher ratio than the negative sentiment during the study period. When zooming into the topic-level analysis, we find that different aspects of COVID-19 have been constantly discussed and show comparable sentiment polarities. Some topics like ``stay safe home" are dominated with positive sentiment. The others such as ``people death" are consistently showing negative sentiment. Overall, the proposed framework shows insightful findings based on the analysis of the topic-level sentiment dynamics.

中文翻译：

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使用社交媒体检测因 COVID-19 大流行而引起的话题和情绪动态

新型冠状病毒病 (COVID-19) 的爆发极大地影响了全球人们的日常生活。各国政府已采取紧急措施和政策（例如封锁、保持社交距离）来对抗这种传染性很强的疾病。然而，由于长期严格的社会隔离规定，人们的心理健康也处于危险之中。因此，在各种事件和主题中监测人们的心理健康对于决策者做出适当的决定是非常必要的。另一方面，社交媒体已被广泛用作人们发表和分享个人观点和感受的渠道。大规模的社交媒体帖子（例如，推文）提供了一个理想的数据源，可以推断大流行期间人们的心理健康状况。在这项工作中，我们提出了一个新颖的框架来分析来自大量社交媒体帖子的 COVID-19 引起的话题和情绪动态。根据两周内收集的 1300 万条与 COVID-19 相关的推文，我们发现在研究期间，积极情绪的比率高于消极情绪。当放大到主题级分析时，我们发现 COVID-19 的不同方面一直在不断讨论，并显示出类似的情绪极性。“宅在家里”等话题以正面情绪为主，“人死”等话题则持续呈现负面情绪。总体而言，所提出的框架基于对主题级情感动态的分析显示了有见地的发现。根据两周内收集的 1300 万条与 COVID-19 相关的推文，我们发现在研究期间，积极情绪的比率高于消极情绪。当放大到主题级分析时，我们发现 COVID-19 的不同方面一直在不断讨论，并显示出类似的情绪极性。“宅在家里”等话题以正面情绪为主，“人死”等话题则持续呈现负面情绪。总体而言，所提出的框架基于对主题级情感动态的分析显示了有见地的发现。根据两周内收集的 1300 万条与 COVID-19 相关的推文，我们发现在研究期间，积极情绪的比率高于消极情绪。当放大到主题级分析时，我们发现 COVID-19 的不同方面一直在不断讨论，并显示出类似的情绪极性。“宅在家里”等话题以正面情绪为主，“人死”等话题则持续呈现负面情绪。总体而言，所提出的框架基于对主题级情感动态的分析显示了有见地的发现。我们发现 COVID-19 的不同方面一直在不断讨论，并显示出类似的情绪极性。“宅在家里”等话题以正面情绪为主，“人死”等话题则持续呈现负面情绪。总体而言，所提出的框架基于对主题级情感动态的分析显示了有见地的发现。我们发现 COVID-19 的不同方面一直在不断讨论，并显示出类似的情绪极性。“宅在家里”等话题以正面情绪为主，“人死”等话题则持续呈现负面情绪。总体而言，所提出的框架基于对主题级情感动态的分析显示了有见地的发现。