Health support has been sought by the public from online social media after the outbreak of novel coronavirus disease 2019 (COVID-19). In addition to the physical symptoms caused by the virus, there are adverse impacts on psychological responses. Therefore, precisely capturing the public emotions becomes crucial to providing adequate support. By constructing a domain-specific COVID-19 public health emergency discrete emotion lexicon, we utilized one million COVID-19 theme texts from the Chinese online social platform Weibo to analyze social-emotional volatility. Based on computed emotional valence, we proposed a public emotional perception model that achieves: (1) targeting of public emotion abrupt time points using an LSTM-based attention encoder-decoder (LAED) mechanism for emotional time-series, and (2) backtracking of specific triggered causes of abnormal volatility in a cognitive emotional arousal path. Experimental results prove that our model provides a solid research basis for enhancing social-emotional security outcomes.
【저자키워드】 COVID-19, deep learning, social-emotional volatility, cause detection, 【초록키워드】 coronavirus disease, Coronavirus disease 2019, social media, media, novel coronavirus disease, virus, Novel coronavirus, outcomes, outbreak, Research, Psychological, public health emergency, platform, mechanism, physical symptoms, psychological responses, emotional, Support, cognitive, Chinese, experimental results, adverse impact, responses, Physical symptom, caused, addition, provide, cause, triggered, Experimental result, 【제목키워드】 volatility, cause,