The field of data analysis is moving towards the development of more interpretable and meaningful representations of large-scale datasets. Topic modeling has emerged as a key tool in this effort, with researchers exploring new methods to enhance the aggregation and visualization of discovered topics. The integration of Formal Concept Analysis (FCA) with topic modeling is showing promise in providing more structured and hierarchical representations of dataset composition. Additionally, there is a growing recognition of the importance of methodological rigour in the application of computational algorithms, including topic modeling. This has led to the development of guidelines and best practices for ensuring transparency and trust in research. Notably, innovative papers such as one proposing a new approach to topic aggregation using FCA and another introducing a measure of distributivity in lattices are advancing the field. Furthermore, a mathematically proven modernized Occam's razor is providing new insights into the development of theoretical models. Particularly noteworthy papers include: one that proposes FAT-CAT, an FCA-based approach to topic aggregation, and another that introduces the concept of rises in lattices to assess distributivity.