ObjectivesThe prolonged coronavirus disease 2019 (COVID-19) pandemic has caused individuals to suffer economic losses, in particular due to the implementation of intensive quarantine policies. Economic loss can cause anxiety and has a negative psychological impact on individuals, worsening their mental health and satisfaction with life. We examined the protective and risk factors that can influence the relationship between economic loss and anxiety during the COVID-19 pandemic.MethodsPanel data from 911 participants were collected in April and May 2020 and again 6 months later. We analyzed the relationship between economic loss and anxiety and investigated the moderating effects of knowledge about COVID-19, gratitude, and perceived stress. Moreover, we investigated whether there were any changes in moderating effects over time or in different demographic groups.ResultsIn the early stages of the spread of COVID-19, gratitude (B = –0.0211, F = 4.8130, p < 0.05) and perceived stress (B = 0.0278, F = 9.3139, p < 0.01) had moderating effects on the relationship between economic loss and anxiety. However, after 6 months, only perceived stress had a significant moderating effect (B = 0.0265, F = 7.8734, p < 0.01).ConclusionIn the early stages of COVID-19, lower levels of gratitude and higher perceived stress led to greater anxiety. In later stages of the prolonged pandemic, only perceived stress had a continued moderating effect on the relationship between economic loss and anxiety. This study suggests that psychological interventions to reduce perceived stress are needed to treat the possible adverse effects of the spread of infectious diseases on mental health.
Understanding emotions in conversation is a challenging task as the sentences often have an implied meaning which is not generally understood in isolation. Efficient use of contextual information is important for emotion recognition in conversations. Many of the published datasets provide contextual information for situations such as text-based online messaging, chatbots, and movie dialogues. However, such dialogue-based datasets are collected by selecting the ideal conversational situations and thus do not include many variations in dialogue length and number of participants. Therefore, such datasets may not be applicable for emotion recognition in text-based movie transcripts, where scenes contain variations in the number of speakers and length of the spoken sentences. We present a conversation dataset based on the Korean TV show transcripts for analysis of the emotions in presence of scene context. Korean Drama Scene Transcript dataset for Emotion Recognition (KD-EmoR) is a text-based conversation dataset. We analyze three classes of complex emotions namely euphoria, dysphoria, and neutral in the scenes of TV Drama to build a publicly available dataset for further research. We developed a context-aware deep learning model to classify the emotions utilizing speaker-level context and scene context and achieved an F1 score of 0.63 on the proposed dataset.
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