# Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis
###### Abstract  
In recent years, the emergence of the smart city concept has garnered attention as a promising innovation aimed at addressing the multifactorial challenges arising from the concurrent trends of urban population growth and the climate crisis. In this study, we delve into the multifaceted dimensions of the smart city paradigm to unveil its underlying structure, employing a combination of quantitative and qualitative techniques. To achieve this, we collected textual data from three sources: scientific publication abstracts, news blog posts, and social media entries. For the analysis of this textual data, we introduce an innovative semi-automated methodology that integrates topic modeling and thematic analysis. Our findings highlight the intricate nature of the smart city domain, which necessitates examination from three perspectives: applications, technology, and socio-economic perspective. Through our analysis, we identified ten distinct aspects of the smart city paradigm, encompassing mobility, energy, infrastructure, environment, IoT, data, business, planning and administration, security, and people. When comparing the outcomes across the three diverse datasets, we noted a relative lack of attention within the scientific community towards certain aspects, notably in the realm of business, as well as themes relevant to citizens' everyday lives, such as food, shopping, and green spaces. This work reveals the underlying thematic structure of the smart city concept to help researchers, practitioners, and public administrators participate effectively in smart city transformation initiatives. Furthermore, it introduces a novel data-driven method for conducting thematic analysis on large text datasets. s +
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last decade [5]. The term \"smart city\" refers to a novel urban environment designed for optimal performance through the integration of ICT and other forms of physical infrastructure [6]. [PERSON] defines the smart city as \"a place where information technology is combined with infrastructure, architecture, everyday objects, and even our bodies to address social, economic, and environmental problems\" [7]. The importance of smart cities lies not only in the technology they employ but also in their innovative approach to addressing urban challenges, improving the quality of life for city residents, and optimizing governmental functions [8]. As interest in smart cities grows, the number of scientific articles being published in this field is rapidly increasing [9], and more valuable information is brought together about how a smart city should be designed, developed, and operated. There is an agreement that this information gathering may help in theory creation by prescribing generalizable models that can be validated by data to describe a complex phenomenon, such as a city [10]. In the context of smart cities, generating analytical outcomes can result in more sophisticated understandings of urban phenomena, thereby aiding the development of evidence-driven urban strategies and fostering innovation. Data science technologies can contribute in various ways to this intention, as elaborated in detail in [9]. In the past, scholars employed qualitative methods to analyze textual, unstructured material for complex phenomena understanding in several fields. Qualitative research entails a thorough examination of texts, leading to a deep human understanding and interpretation [11]. However, these manual techniques are not only time-consuming and labor-intensive, but they also present issues owing to coding bias and a lack of uniformity in objectivity [12].