The Early Emotional Responses and Central Issues of People in the Epicenter of the COVID-19 Pandemic: An Analysis from Twitter Text Mining

This study aimed to explore citizens’emotional responses and issues of interest in the context of the coronavirus disease 2019(COVID-19)pandemic.The dataset comprised 65,313 tweets with the location marked as New York State.The data collection period was four days of tweets when New York City imposed a lockdown order due to an increase in confirmed cases.Data analysis was performed using R Studio.The emotional responses in tweets were analyzed using the Bing and NRC(National Research Council Canada)dictionaries.The tweets’central issue was identified by Text Network Analysis.When tweets were classified as either positive or negative,the negative sentiment was higher.Using the NRC dictionary,eight emotional classifications were devised:“trust,”“fear,”“anticipation,”“sadness,”“anger,”“joy,”“surprise,”and“disgust.”These results indicated that citizens showed negative and trusting emotional reactions in the early days of the pandemic.Moreover,citizens showed a strong interest in overcoming and coping with other people such as social solidarity.Citizens were concerned about the confirmation of COVID-19 infection status and death.Efforts should be made to ensure citizens’psychological stability by promptly informing them of the status of infectious disease management and the route of infection.

A semantic and emotion-based dual latent variable generation model for a dialogue system

With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses.However,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response.We propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional dictionary.Different from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer structure.Then,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion.The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively.Moreover,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model.Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.