Written language is the primary means by which scientific research findings are disseminated. Yet in the era of information overload, dissemination of a field of research may require additional efforts given the sheer volume of material available on any specific topic. Topic models are unsupervised natural language processing methods that analyze nonnumeric data (i.e., text data) in abundance. These tools aggregate, and make sense of, those data making them interpretable to interested audiences. In this perspective piece, we briefly describe topic models, including their purpose, function, and applicability for health education researchers and practitioners. We note how topic models can be applied in several contexts, including social media–based analyses, and mapping trends in scientific literature over time. As a tool for studying words, and patterns of words, topic models stand to improve our understanding of events prior and those occurring in the moment and help us look ahead into the future.