LDA is a type of unsupervised machine learning model that is used to identify the topics present in a set of documents. It assumes that each document is a mixture of a small number of topics and that each word's presence is attributable to one of the document's topics. LDA is used for topic modeling, where it helps in identifying topic distributions in a corpus. It's particularly useful for finding the underlying themes in large volumes of text. It's valuable for a wide range of applications in text mining, information retrieval, and natural language processing. ### Key Concepts: - **Document-Topic Distribution:** Represents the mixture of topics that a document contains. For instance, a document might be 30% about environment, 20% about politics, and 50% about economy. - **Topic-Word Distribution:** Represents the distribution of words for a given topic. It tells you which words are most likely to appear in a given topic. `gensim`