Topic Modeling and Text Clustering
Discover open-source libraries and algorithms for extracting thematic structures and grouping large-scale text document collections.
Topic Modeling and Text Clustering
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- rare-technologies/gensim
RaRe-Technologies/gensim
16,442View on GitHubGensim is an unsupervised natural language processing toolkit designed for topic modeling, word embedding training, and the processing of large-scale text corpora. It provides a framework for discovering latent themes and semantic structures in text without the need for labeled data. The toolkit is distinguished by its ability to handle datasets that exceed system memory through iterator-based data streaming from disk. It also supports distributed model training, allowing complex modeling tasks to be executed across computer clusters. The library covers a broad range of analysis capabilities
Gensim is a mature Python NLP toolkit built specifically for topic modeling on large text corpora, offering LDA and NMF implementations, built-in topic coherence evaluation, and scalable streaming—so it directly meets your requirements.
PythonTopic Models - maartengr/bertopic
MaartenGr/BERTopic
7,403View on GitHubBERTopic is a topic modeling library used to extract interpretable themes from collections of text documents and images. It functions as a document clustering framework that transforms unstructured data into numerical vectors to group semantically similar content. The project distinguishes itself through a multimodal embedding tool that allows for joint clustering of text and images in a shared vector space. It also features a class-based TF-IDF representation engine to identify representative words for clusters and an integrated system for using large language models to generate natural lang
BERTopic is a topic modeling library that uses embedding-based clustering and class-based TF-IDF instead of the LDA or NMF algorithms you requested, but it is a full-fledged Python library with visualization tools and topic coherence evaluation, fitting the topic modeling category.
PythonTopic Projection VisualizationsOnline LearningVisual Topic Summaries - lda-project/lda
lda-project/lda
1,313View on GitHubThis project is a statistical library and computational framework designed for topic modeling within large document collections. It functions as a natural language processing toolkit that identifies hidden thematic structures by analyzing word frequency patterns across unstructured text data. The library employs Latent Dirichlet Allocation to model documents as mixtures of topics and topics as mixtures of words. It utilizes Gibbs sampling and iterative state-space updating to estimate the posterior distribution of latent variables, refining topic assignments until the model reaches statistica
The lda library implements LDA topic modeling via Gibbs sampling in Python, providing the core capability requested, but it lacks NMF, topic coherence evaluation, and built-in visualization, so it is a solid but narrower fit.
PythonLatent Dirichlet Allocations - milanlproc/contextualized-topic-models
MilaNLProc/contextualized-topic-models
1,271View on GitHubContextualized topic modeling is a framework that integrates deep learning architectures with statistical word frequency distributions to extract coherent themes from large document collections. By combining pre-trained transformer-based embeddings with variational inference, the system identifies hidden patterns in text while maintaining the interpretability of traditional generative models. The library distinguishes itself by mapping diverse languages into a shared semantic space, enabling topic discovery and classification across multilingual datasets without requiring language-specific tr
Contextualized Topic Models is a Python library for neural topic modeling using BERT embeddings, which squarely fits the category of topic modeling libraries but uses modern contextualized approaches rather than traditional LDA or NMF—it supports topic coherence and is Python-based, though it may not include built-in visualization or explicit large-corpus scalability.
PythonLatent Dirichlet AllocationsTopic Models
