Topic modeling algorithms (LDA, NMF, PLSA)

Topic Modeling Algorithms (LDA, NMF, PLSA) are a set of algorithms which allow for the automated identification of topics contained within a document. Topic modeling is a powerful tool for finding key topics or topics clusters in large collections of documents, such as large corpora.

Latent Dirichlet Allocation (LDA) is a generative statistical model which is used for uncovering the topics in a corpus or a document. It is based on a probabilistic generative approach, which assumes topics as latent variables. Given a corpus of documents, LDA infers the presence of topics that explain the corpus. The way topics can be generated using LDA is by giving input the corpus to the model and extracting relevant topics.

Non-Negative Matrix Factorization (NMF) is a powerful approach for uncovering the underlying topics in document collections. NMF is based on the assumption that there exist distinct components in each document and that these components represent various topics. NMF also has the ability to extract topics from very large corpora efficiently.

Probabilistic Latent Semantic Analysis (PLSA) is an unsupervised learning algorithm used for topic modeling from text documents. It is based on the assumption that each document contains several topics and that a term generated from that document will contribute to one or more of those topics. PLSA is a Statistical Latent Variable Model and estimates the probability distributions for the topics and terms.

Topic modeling algorithms such as LDA, NMF, and PLSA are powerful tools for uncovering the latent topics in a collection of documents. Topics generated using these algorithms can be used to identify key topics in large corpora, classify documents, and analyze text clusters. Topic modeling algorithms have also been used for a variety of other tasks such as text summarization and sentiment analysis.