Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion detection and analysis, based on the Plutchik/Ekman approach to emotion detection and trend detection. We present an evaluation of the framework and a pilot system. Results of confirm the effectiveness of the proposed framework for topic trends and emotion detection of COVID-19 tweets. Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive emotional semantics. Semantic trends of safety issues related to staying at home rapidly decreased within the 28 days and also negative feelings related to friends dying and quarantined life increased in some days. These findings have potential to impact public health policy decisions through monitoring trends of emotional feelings of those who are quarantined. The framework presented here has potential to assist in such monitoring by using as an online emotion detection tool kit.

中文翻译：

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人工智能在COVID-19社交隔离期间的情绪-语义趋势和人的情绪检测

利用诸如Twitter之类的社交媒体平台，本文为隔离对象中的情绪检测提供了有效的框架。尽早发现情绪感受及其趋势有助于实施及时的干预策略。鉴于隔离期间对早期情绪变化征兆进行医学诊断的局限性，人工智能模型提供了揭示早期征兆，症状和逐步发展趋势的有效机制。本文介绍的方法的新颖性是文本数据处理的多任务方法框架，基于用于情感检测和趋势检测的Plutchik / Ekman方法，被实现为有意义的情感检测和分析的管道。我们对框架和试验系统进行评估。结果证实了所提出的框架对COVID-19推文的主题趋势和情绪检测的有效性。我们的发现表明，“居家”限制导致人们在推特上表达负面和正面的情感语义。与待在家里有关的安全问题的语义趋势在28天内迅速减少，并且与朋友垂死和隔离生活有关的负面情绪在某些天内有所增加。这些发现有可能通过监视被隔离人员的情感趋势来影响公共卫生政策的决策。这里介绍的框架有潜力通过用作在线情绪检测工具套件来协助进行此类监视。我们的发现表明，“居家”限制导致人们在推特上表达负面和正面的情感语义。与待在家里有关的安全问题的语义趋势在28天内迅速减少，并且与朋友垂死和隔离生活有关的负面情绪在某些天内有所增加。这些发现有可能通过监视被隔离人员的情感趋势来影响公共卫生政策的决策。这里介绍的框架有潜力通过用作在线情绪检测工具套件来协助进行此类监视。我们的发现表明，“居家”限制导致人们在推特上表达负面和正面的情感语义。与待在家里有关的安全问题的语义趋势在28天内迅速减少，并且与朋友垂死和隔离生活有关的负面情绪在某些天内有所增加。这些发现有可能通过监视被隔离人员的情感趋势来影响公共卫生政策的决策。这里介绍的框架有潜力通过用作在线情绪检测工具套件来协助进行此类监视。这些发现有可能通过监视被隔离人员的情感趋势来影响公共卫生政策的决策。这里介绍的框架有潜力通过用作在线情绪检测工具套件来协助进行此类监视。这些发现有可能通过监视被隔离人员的情感趋势来影响公共卫生政策的决策。这里介绍的框架有潜力通过用作在线情绪检测工具套件来协助进行此类监视。