Topic modeling
28 methods in this family.
Featured
BERTopicBERTopic is a neural topic-modeling pipeline introduced by Maarten Grootendorst in 2022. It combines BERT-based contextual embeddings with UMAP dimensionality reduction and HDBSCANDomain-adaptive NMF Topic ModelDomain-adaptive NMF Topic Modeling applies Non-negative Matrix Factorization to discover latent topics across text from multiple domains, using regularization or shared basis constExplainable LDA Topic ModelExplainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretabiExplainable NMF Topic ModelAn Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such aExplainable Topic ModelingExplainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scoreFine-Tuned LDA Topic ModelFine-Tuned LDA adapts a Latent Dirichlet Allocation model trained on a large general corpus to a specific target domain by continuing inference on domain-specific documents. Rather
All methods 28
BERTopicDomain-adaptive NMF Topic ModelExplainable LDA Topic ModelExplainable NMF Topic ModelExplainable Topic ModelingFine-Tuned LDA Topic ModelFine-Tuned Topic ModelingLatent Dirichlet AllocationLDA Topic ModelMultilingual topic modelingMultimodal LDA topic modelMultimodal NMF Topic ModelMultimodal Topic ModelingNMF Topic ModelNMF Topic ModelingSelf-supervised LDA Topic ModelSelf-supervised NMF Topic ModelSelf-supervised topic modelingSemi-supervised LDA Topic ModelSemi-supervised NMF Topic ModelSemi-supervised Topic ModelingTopic ModelingTopic Modeling (LDA)Transfer Learning with LDA Topic ModelTransfer Learning with NMF Topic ModelTransfer Learning with Topic ModelingWeakly supervised LDA topic modelWeakly Supervised Topic Modeling