A Multi-layered Psychological-Based Reference Model for Citizen Need Assessment Using AI-Powered Models

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.