Topic modeling methods are algorithms that help us understand the main themes in a large collection of text. They group words with similar meanings and show us the hidden structure in documents. The study looked at different techniques like Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, and Latent Dirichlet Allocation, along with some variations of LDA. The researchers explored the characteristics, limitations, and real-world uses of these methods.