Diurnal emotions, valence and the coronavirus lockdown analysis in public spaces. (February 2021)

• Record Type: Journal Article
• Authors: Kaklauskas, Arturas; Abraham, Ajith; Milevicius, Virgis
• Is Part Of: Engineering applications of artificial intelligence. Volume 98(2021)
• Publication Date: 2021-02
• Subjects: Diurnal emotions -- Valence and facial temperature -- COVID-19 -- Public spaces -- Remote biometric technologies -- Large-scale data analysis -- Worldwide comparisons; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.104122
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: A large-scale analysis of diurnal and seasonal mood cycles in global social networks has been performed successfully over the past ten years using Twitter, Facebook and blogs. This study describes the application of remote biometric technologies to such investigations on a large scale for the first time. The performance of this research was under real conditions producing results that conform to natural human diurnal and seasonal rhythm patterns. The derived results of this, 208 million data research on diurnal emotions, valence and facial temperature correlate with the results of an analogical Twitter research performed worldwide (UK, Australia, US, Canada, Latin America, North America, Europe, Oceania, and Asia). It is established that diurnal valence and sadness were correlated with one another both prior to and during the period of the coronavirus crisis, and that there are statistically significant relationships between the values of diurnal happiness, sadness, valence and facial temperature and the numbers of their data. Results from the simulation and formal comparisons appear in this article. Additionally the analyses on the COVID-19 screening, diagnosing, monitoring and analyzing by applying biometric and AI technologies are described in Housing COVID-19 Video Neuroanalytics. Highlights: Remote biometric technologies and AI were employed for the first time to analyze passersby on a large scale. The 208 million items of data on diurnal emotions, valence and facial temperature, taken for this study, correlate with Twitter worldwide data. These research results confirm natural, human, diurnal and seasonal rhythm patterns. Diurnal valence and sadness correlated with each other, both prior to and during COVID-19. The relationships established between the biometric data and their numbers proved statistically significant.