Topic modeling and LDA (Latent Dirichlet Allocation) have proven valuable in various fields as an innovative approach to studying ar- eas of interest and identifying topics in a dynamic content. The un- derlying assumption is that techniques like LDA can swiftly capture emerging topics in textual documents compared to other categoriza- tion tools. These unsupervised approaches have been used to identify new industries and technological domains. However, our study on the nascent topic of “sustainability” within the corpora of SGX-listed companies highlights clear limitations in employing techniques like LDA on sparse data. The dynamic LDA approach, also called DTM (Dynamic Topic Modelling),based on an 11-year database of annual reports from publicly listed companies in Singapore, could not detect sustainability’s rise as a critical topic in corporate practice following policy changes. Moreover, despite sustainability reporting becoming mandatory, sustainability-related topics may still not receive signifi- cant attention.