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.
翻译:本文利用Twitter等社交媒体平台,提供了一个有效框架,用于在被隔离者中检测情绪; 早期发现情感情感及其趋势有助于实施及时的干预战略; 鉴于隔离期间对早期情感变化迹象的医疗诊断有限,人工智能模型为发现早期迹象、症状和不断上升的趋势提供了有效的机制; 本文介绍的方法是一个文本数据处理的多任务方法框架,它是在Plutchik/Ekman对情感检测和趋势检测采取Plutchik/Ekman方法的基础上,作为有意义情感检测和分析的管道实施的。 我们介绍了对框架和试点系统的评估。 证实拟议的COVID-19 Twitter主题趋势和情绪检测框架的有效性的结果。 我们的调查结果揭示了长期-At-Home限制导致人们在微博上表达负面和积极的情绪语义。 与在28天内呆在家里有关的安全问题的神秘性趋势在28天内迅速下降,与朋友死亡和隔离生活有关的消极感觉在几天内增加。这些发现有可能通过监测那些被隔离的人的情感感觉来影响公共卫生政策决定。 本文中展示了这种工具用于在线检测。