The article provides a comprehensive overview of topic modeling, a process used in natural language understanding to identify and extract topics from a collection of documents. It discusses several popular techniques, including Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (pLSA), Latent Dirichlet Allocation (LDA), and the deep learning-based lda2vec. LSA utilizes a document-term matrix and Singular Value Decomposition for dimensionality reduction, while pLSA applies a probabilistic approach to model topics. LDA, a Bayesian extension of pLSA, introduces Dirichlet distributions to enhance generalization, especially for new documents. The article also describes lda2vec, which integrates word2vec and LDA to jointly learn word, document, and topic vectors. Each method has its strengths and limitations, and the text emphasizes understanding the underlying mathematics and intuition behind these models to leverage them effectively in various applications.