Gensim

Gensim is an open-source Python library designed for topic modeling and document similarity analysis. It is particularly well-suited for processing large text corpora, allowing users to extract semantic topics from documents efficiently. Gensim utilizes algorithms like Latent Dirichlet Allocation (LDA) and Word2Vec to create vector representations of words and documents, making it easier to analyze and compare textual data. Common use cases include building recommendation systems, clustering documents, and performing information retrieval tasks.