Topic modeling algorithms (LDA, NMF, PLSA) are algorithms used to find hidden thematic structure in large collections of text documents. They are frequently used in natural language processing (NLP) to discover themes in a text. While all three algorithms are well known and have seen frequent use, Latent Dirichlet Allocation (LDA) is the most commonly used.
Latent Dirichlet Allocation (LDA) is a generative statistical model used for uncovering the thematic structure of a collection of documents. The algorithm identifies topics by uncovering clusters of words or phrases that frequently appear in the same documents. This approach is powerful because it allows for unsupervised learning which does not require labeled data.
Non-Negative Matrix Factorization (NMF) is an algorithm that finds hidden structure in a collection of documents. Like LDA, the algorithm discovers clusters of words that often appear together in documents. However, unlike LDA, NMF requires the input data to be non-negative.
Probabilistic Latent Semantic Analysis (PLSA) is an algorithm similar to LDA, but with a few key differences. First, PLSA does not factor in topic-specific terms, and instead searches for latent topics shared across documents. Secondly, the algorithm assumes that documents are generated by a certain set of topics, which is likely to be inaccurate in some cases.
In conclusion, topic modeling algorithms (LDA, NMF, PLSA) are important methods in natural language processing (NLP) that can be used to uncover hidden themes in a text. LDA is the most commonly used algorithm for this purpose, and is especially powerful due to its ability to perform unsupervised learning without requiring labeled data.
