  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.
