Emotion analysis has been attracting researchers' attention. Most previous works in the artificial-intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. The correlation among emotions contributes to the failure of emotion recognition. In this article, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from Web news. The correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural-network models are presented. The emotion confusion law is extracted through an orthogonal basis. The emotion evolution law is evaluated from three perspectives: one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets: 1) the titles; 2) the bodies; and 3) the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love-anger and sadness-anger. In objective news, it is easy to recognize text emotion as love and cause fear-joy circulation. These findings could provide insights for applications regarding affective interaction, such as network public sentiment, social media communication, and human-computer interaction.

Mathematical and computational models are useful tools for virtual policy experiments on infectious disease control. Most models fail to provide flexible and rapid simulation of various epidemic scenarios for policy assessment. This paper establishes a multi-scale agent-based model to investigate the infectious disease propagation between cities and within a city using the knowledge from person-to-person transmission. In the model, the contact and infection of individuals at the micro scale where an agent represents a person provide insights for the interactions of agents at the meso scale where an agent refers to hundreds of individuals. Four cities with frequent population movements in China are taken as an example and actual data on traffic patterns and demographic parameters are adopted. The scenarios for dynamic propagation of infectious disease with no external measures are compared versus the scenarios with vaccination and non-pharmaceutical interventions. The model predicts that the peak of infections will decline by 67.37% with 80% vaccination rate, compared to a drop of 89.56% when isolation and quarantine measures are also in place. The results highlight the importance of controlling the source of infection by isolation and quarantine throughout the epidemic. We also study the effect when cities implement inconsistent public health interventions, which is common in practical situations. Based on our results, the model can be applied to COVID-19 and other infectious diseases according to the various needs of government agencies.

No abstract

No abstract