A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System

While Huge amounts of unstructured, textual data are most of these studies did not feed the refinement operations generated daily. As more data becomes available, into an iterative retraining process, Smith it becomes more difficult to search, understand et al. (2018) implemented a fully interactive, usercentered and discover the knowledge within it. Because of HL-TM system, and examined how the the human effort it requires, conventional qualitative user experience is affected by issues arising in interactive approaches, such as Grounded Theory, (Glaser systems, such as unpredictability, trust and et al., 1968) are no longer feasible with such large lack of control. However, there are still limitations volumes of data. Topic modelling is a potential to their work. First, their system only allows users solution that has received increasing attention in to refine the model sequentially, meaning that once recent research (Heidenreich et al., 2019; Curiskis a user updates the model, a new model overrides et al., 2020; Dantu et al., 2021; Goyal and Howlett, the previous model. This prevents users from comparing 2021) to help users organize, search, and understand the effects of applying different refinement large amounts of information. It is an unsupervised operations to the same model, making it difficult machine learning technique for identifying to find the most appropriate ones.