documenttopic

Documenttopic is a term used to describe the thematic composition assigned to a document by topic modeling methods. In typical formulations, each document is represented by a topic distribution, often denoted theta_d, which gives the probability mass across a set of latent topics. The topics themselves are distributions over words and are learned from a corpus.

The most common approach is Latent Dirichlet Allocation (LDA), where documents are mixtures of topics and topics are mixtures of words; other approaches include non-negative matrix factorization (NMF), probabilistic topic models such as Correlated Topic Models, and neural topic models like ProdLDA or variational autoencoder–based models. Dynamic topic models track documenttopic evolution over time.

Interpreting documenttopic requires inspecting the topic-word distributions to label topics; the documenttopic vectors can be used for downstream tasks such as document clustering, matching queries to documents, recommender systems, or content tagging.

Evaluation uses perplexity on held-out data and topic coherence measures such as UMass, CV, or C_P, with coherence generally correlating with human judgments. Short texts and multilingual corpora present challenges, and choosing the number of topics remains a practical consideration in modeling.

See also: topic modeling, LDA, NMF, neural topic models, topic coherence.