Using Artificial Intelligence to Monitor the Emotional Pulse of Smart City Residents

Abstract

Industrial informatics has been paying increasing attention to smart city applications during the last decade. However, individuals' emotions and impressions, which have a direct influence on smart city programmes, have gotten little attention. A city's 'emotional pulse' may be gauged via the use of publicly accessible, plentiful social media discussions that include contextual information about residents' feelings and perspectives. Emotions and negativity may be detected using an automated AI-based observation system. As self-driving cars become more prevalent in smart cities, we used 29,928 social media chats to gauge the framework's applicability. The NLP and Markov models were used to predict the patterns and transitions of citizens' collective emotions, and a deep learning-based classifier was used to assess the negativity (toxicity) in talks. In order to increase security, communication and policymaking, business executives as well as government authorities may use this framework.