Emotion detection and semantic trends during COVID-19 social isolation using artificial intelligence techniques

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 helps to implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence (AI) 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-semantic analysis and trend detection. Regarding the significant applications of this research work, the experimental results revealed that our AI-based emotion-semantic aspects can effectively uncover people’s emotional reactions during a pandemic, especially when abiding to the stay-at-home preventive measure. Moreover, the research can be applied to uncover reactions to similar public health policies that affect people’s well-being.