Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation

This more than a yearlong outbreak is likely to have a significant impact on mental health of many individuals who lost loved ones, who lost personal contacts with others due to strictly enforced public health guidelines of mandatory social segregation. Complex psychological reactions to COVID-19 regulatory mechanisms of mandatory quarantine and related emotional reactions has been recognized as hard to disentangle [1] - [4]. A study conducted in Belgium found social media being positively associated with constructive coping for adolescents with anxious feelings during the quarantine period of COVID-19 [4]. Another study conducted among social media users during COVID-19 pandemic in Spain was able to capture added stress placed on people's emotional health during the pandemic period [5]. However, social media providing a platform of risk communication and exchange of feelings and emotions to curb social isolation, this text data provides a wealth of information on the natural flow of people's emotional feelings and expressions [6]. This rich source of data can be utilized to curb the data collection barriers during the pandemic. The goal of this research was to use AI to uncover the hidden, implicit signal related to emotional health of people subject to mandatory quarantine, embedded in a latent manner in their twitter messages. Within the context of this paper, an NLPbased emotion detection system aims to provide useful information by examining unstructured text data used in social media. The purpose of the NLP system used herein is to show the meaning and emotions of users' expressions related to a particular topic, which can be used to understand their psychological health and emotional wellbeing.