Understanding Citizens' Emotional Pulse in a Smart City Using Artificial Intelligence

• Published in: IEEE transactions on industrial informatics 2021-04, Vol.17 (4), p.2743-2751
• Main Authors: Adikari, Achini, Alahakoon, Damminda
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Autonomous cars; Computational modeling; Deep learning; Digital media; Emotions; emotions modeling; Evaluation; Informatics; Markov chains; Markov models; Natural language processing; Natural Language Processing (NLP); Smart cities; smart city; Social networks; Toxicity; Twitter; Verbal communication
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.3009277

Over the past decade, smart city applications have gained significant attention in industrial informatics. However, little attention has been given to perceiving the emotions and perceptions of citizens who have a direct impact on smart city initiatives. In this article, we propose the use of publicly available abundant social media conversations that contain contextual information encompassing citizens' emotions and perceptions, which could be considered to provide the means to feel the "emotional pulse" of a city. We propose an automated AI-based observation framework to detect the emergence of public emotions and negativity in conversations. We evaluated the applicability of the framework using 29 928 social media conversations toward the much-debated topic of self-driving vehicles which will become increasingly relevant to smart cities. The patterns and transitions of citizens' collective emotions were modeled using the Natural Language Processing and Markov models while the negativity (toxicity) in conversations was evaluated using a deep learning based classifier. The framework could be adopted by industry leaders and government officials for smart observation of citizen opinions to improve security, communication, and policymaking.