This chapter provides an initial review of topic modeling, a group of approaches for uncovering hidden text themes. The chapter concentrates on one popular method, Latent Dirichlet Allocation (LDA), a probabilistic machine learning technique that excels in reconstructing hidden recurrent topics present in large sets of textual documents. Unlike clustering methods discussed in Chapter 11 , LDA views documents as mixtures of topics and topics as distributions over words. This chapter explains the best practices of topic modeling, including data preprocessing, cleaning, standardization, model hyperparameters selection, and the optimal number of topics. The chapter concludes by comparing LDA to more advanced methods like Large Language Models (Chapter 13 ), emphasizing LDA's simplicity, transparency, and effectiveness in interpretable topic modeling for cohesive datasets. In the practical lab, the reader develops a Python code to analyze a dataset of visitor reviews from Arenal Volcano National Park (Costa Rica), showcasing LDA’s ability to identify coherent topics like waterfalls, volcanoes, and hot springs.