A Multi-layered Psychological-Based Reference Model for Citizen Need Assessment Using AI-Powered Models
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ORIGINAL RESEARCH
A Multi‑layered Psychological‑Based Reference Model for Citizen Need Assessment Using AI‑Powered Models Rajwa Alharthi1,2 · Abdulmotaleb El Saddik1 Received: 30 April 2020 / Accepted: 26 July 2020 © The Author(s) 2020
Abstract We propose an automatic, low-cost, large-scale, nonintrusive human need recognition framework that utilized a multi-layered psychological-based reference model and designed with different modules including data collection, preprocessing, feature extraction and contextualization module. The reference model comprises several classification and regression models to identify human psychological needs, measure their satisfaction levels, evaluate their surrounding environment around different life aspects during any subjective event or towards emerging topics at any time, and in any location, using their publicly available social media content. We evaluate the predictive powers of various textual, psychological, semantic, lexicon-based and Twitter-specific features. To provide benchmark results, we compare and evaluate the performance of diverse machine learning algorithms. Our results confirm the effectiveness of the developed reference model. The framework is used to recognize citizen needs in response to the New Zealand terror attacks which occurred on March 15th, 2019. Keywords Smart cities · Citizen needs · Reference model · Machine learning · Affect-aware city · Critical event · Need satisfaction · Social media analytics
Introduction Urban innovation and solutions driven by Information and Communication Technologies (ICT) have been progressively applied to enhance urban life in terms of economy, mobility, environment, people, living and governance. The realization of a true smart city vision is now closer than ever [1]. More and more, the number of applications and services that are adopting these technologies with the intention of improving the performance of urban services which will, in turn, enhance the quality of life of citizens is growing [2]. For these applications to be effective, a variety of sensors are needed to continuously collect near real-time data. Currently, urban planners in a smart city rely mostly on the data obtained from measurement equipment or physical sensors * Rajwa Alharthi [email protected]; [email protected] Abdulmotaleb El Saddik [email protected] 1
Multimedia Communications Research Laboratory, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Department of Computer Science, Taif University, Taif 26571, Saudi Arabia
2
“hard sensors” such as cameras, environmental sensors, implanted medical devices, or telematics systems in vehicles [3]. The data retrieved from the deployed sensors are inserted into a large computing platform and then aggregated to provide a unified view of the city. Authorities then reference these data in making informed decisions on the management of the city and its events. The data retrieved from hard sensors, however, do not directly reflect the fluid response of people regarding changes in their immed
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