A topic model describes text from a large corpus as a probability distribution over topics which are probability distributions over words. There are quantified contributions from all topics to a specific text.
A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats (source: wikipedia)
Generative models (i.e. the statistical models used for topic modelling)
- Latent Dirichlet Allocation (LDA)
- Hierarchical Dirichlet process (HDP)
- Non-Negative Matrix Factorisation
Software / Libraries
- Mallet (Java)
- Stanford Topic Modeling Toolbox (software)
- Gensim – Topic Modelling for Humans
- lda (R)
