# Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis
## 2 Materials and Methods  
### Research Design  
This study aims to propose a framework for identifying the key aspects of a smart city as they emerge from the thematic analysis of three distinct sources: (a) scientific publications, (b) a business news blog, and (c) a social media platform. The three specific sources were chosen to collect information from academia, businesses, and citizens, respectively. The goal was to establish a foundation for smart city design, development, and management. Specifically, the study begins by investigating emerging topics through an analysis of 13,473 abstracts of pertinent scientific articles, 3224 blog posts, and 2370 social media entries by leveraging a state-of-the-art machine-learning (ML) technique for topic modeling. Subsequently, this paper identifies and compares the topics extracted from the three diverse text corpora, conducting a thorough study of the most representative documents to facilitate a comprehensive interpretation of the investigated domain. This present article is guided by the following research questions:  
RQ1: What are the key aspects of the smart city concept? RQ2: Which themes within the smart city domain require increased emphasis and attention from researchers? To achieve our goals, we propose a methodology for investigating the key aspects of a smart city design by employing thematic analysis using both quantitative and qualitative techniques. Many researchers commonly acknowledge that each quantitative and qualitative research technique possesses its own set of strengths and limitations. As a result, it is often recommended to integrate both approaches to ensure that their outcomes harmonize and enhance each other [28]. A typical workflow for thematic analysis is proposed by [PERSON] and [PERSON] [15], including six phases, as illustrated in Figure 1. They presented a comprehensive guide that entails a recursive process, allowing for movement back and forth as required across the phases. Thematic analysis embarks with a phase of familiarization with the data, during which researchers begin by comprehensively reading the data to develop a holistic understanding of its content before delving into detailed analysis. This is followed by a coding process involving an in-depth examination of the data. Significant excerpts from the data are selected, and \"codes\" are applied to them. These codes are brief expressions or single words that encapsulate and categorize the text's subject matter. The codes should possess a level of generality that allows them to be applicable across various sections of the text, thus uniting disparate passages discussing the same topic. After completing the initial close coding, the researcher revisits the codes to identify overarching themes by amalgamating codes and exploring their interconnections. Once a series of themes have been extracted, they are assessed and refined by revisiting the original data and comparing them to ensure the alignment of the themes with the data [11]. However, text mining can offer an initial examination of the text, showcasing primary subjects and trends, before engaging in a qualitative assessment of the documents [29]. The fundamental challenges related to thematic analysis revolve around identifying a theme and determining the number of themes within a given dataset. Topic modeling not only enables the exploration of a massive body of text but also facilitates a more systematic identification of themes. Topics consist of probabilistic word distributions. Grounded in ML and natural language processing (NLP), topic modeling can be employed to depict our documents as probabilistic distributions of topics. Leveraging our awareness of the words and their frequencies in documents, we can utilize this information to construct these topic models. Subsequently, having established our topic model, we can commence portraying all our documents through topic distributions [30].