“Big Data Analytics” has become a high priority topic in Cyber Research and in the field of Cyber Security, Big Data represents a very serious problem. In the domain of Digital Forensics Investigations (DFI), the sheer volume of data to be analyzed impedes police operations that require timely reporting of DFI results to support active criminal investigations in the field. In the domain of Cyber Threat Intelligence (CTI), a rapid assessment of the available threat data is required to enable dissemination of actionable intelligence in a timely manner. Topic Modeling is an unsupervised machine learning method for analyzing large bodies of text data and producing estimates of the topics under discussion in them. To gain some insight into how it works, we reviewed some of the underlying principles of Topic Modeling. Then, I presented experimental results that show how Topic Modeling would work in the specific domains of DFI (using the Enron data set) and CTI (using posts scraped from an online hacker forum).