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.
Highlights
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 interpretation of data garnered through the proposed psychological need recognition and analysis can assist authorities providing a heightened situational awareness and improving management of pre- and post-event conflicts and social reactions
We design, implement, and evaluate a theoretical-based multi-layered reference model, which is capable of identifying human psychological needs, and ascertaining their satisfaction level, evaluating people surrounding environment with regard to various life aspects
Summary
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 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. The data retrieved from the deployed sensors are inserted into a large computing platform and aggregated to provide a unified view of the city Authorities reference these data in making informed decisions on the management of the city and its events.
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