## 1 Introduction  
Qualitative data provide thick contextual information [1; 2; 3; 4] that can support reliable complex system model development. Qualitative data analysis explores systems components, their complex relationships, and behavior [3; 4; 5]) and provides a structured framework that can guide the formulation of quantitative models [6; 7; 8; 9; 10]. However, qualitative research is complex, and time- and resource-consuming [1; 4]. Data analysis usually involves keyword-based data extraction and evaluation that requires multiple coders to reduce biases. Moreover, model development using qualitative data requires multiple, lengthy, and expensive stakeholder interactions [11; 12], which adds to its inconvenience. Consequently, quantitative modelers often avoid using qualitative data for their model development. Modelers often skip qualitative data analysis or use unorthodox approaches for framework development, which may lead to failed capturing of target systems' complex dynamics and produce inaccurate and unreliable outputs [13]. The development in the information technology sector has substantially increased access to qualitative data over the past few decades. Harvesting extensive credible data is crucial for reliable model development. Increased access to voluminous data presents a challenge and an opportunity for model developers [14]. However, qualitative data analysis has always been a hard nut to crack for complex modelers. Most existing qualitative data analyses are highly supervised (i.e., performed mainly by humans) and hence, bias-prone and inefficient for large datasets. This study proposes a methodology that uses an efficient, largely unsupervised qualitative data extraction for credible Agent-Based Model (ABM) development using Natural Language Processing (NLP) toolkits. ABM requires information on agents (emulating the target system's decision makers), their attributes, actions, and interactions for its development. The development of a model greatly depends on its intended purpose. Abstract theoretical models concentrate on establishing new relationships and theories with less emphasis on data requirements and structure. In contrast, application-driven models aim to explain specific target systems and tend to be data-intensive. They require a higher degree of adherence to data requirements, validity, feasibility, and transferability [15; 16; 17]. Our methodology is particularly applicable to application-driven models rich in empirical data. ABMs help understand phenomena that emerge from nonlinear interactions of autonomous and heterogeneous constituents of complex systems [18; 19; 20]. ABM is a bottom-up approach; interactions at the micro-level produce complex and emergent phenomena at a macro (higher) level. As micro-scale data become more accessible to the research community, modelers increasingly use empirical data for more realistic system representation and simulation [11; 21; 22; 23; 24]. Quantitative data are primarily useful as inputs for parameterizing and running simulations. Additionally, quantitative model outputs are also used for model verification and validation. Qualitative data, on the other hand, find uses at various stages of the model cycle [25]. Apart from the routine tasks of identifying systems constituents and behaviors for model development, qualitative data support the model structure and output representations [26; 27]. Qualitative model representations facilitate communication for learning, model evaluation, and replication. Various approaches have been proposed to conceptualize computational models. First of all, selected quantitative models have predefined structures for model representation. System dynamics, for instance, uses Causal Loop Diagrams as qualitative tools [28]. Causal Loop Diagrams elucidate systems components, their interrelationships, and feedback that can be used for learning and developing quantitative system dynamics models.