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awesome-topic-models

✨ Awesome - A curated list of amazing Topic Models (implementations, libraries, and resources)
https://github.com/jonaschn/awesome-topic-models

Last synced: about 16 hours ago
JSON representation

  • Models

    • Topic Models for short documents

    • Truncated Singular Value Decomposition (SVD) / Latent Semantic Analysis (LSA) / Latent Semantic Indexing (LSI)

      • SVDlibc - C implementation of SVD by Doug Rohde
      • sparsesvd - Python wrapper for SVDlibc
      • sparsesvd - Python wrapper for SVDlibc
      • gensim - Python implementation using multi-pass [randomized SVD solver](https://arxiv.org/pdf/0909.4061.pdf) or a [one-pass merge algorithm](https://rdcu.be/cghAi)
      • BIDMach - Scala implementation of a scalable approximate SVD using subspace iteration
      • scikit-learn - Python implementation using fast [randomized SVD solver](https://arxiv.org/pdf/0909.4061.pdf) or a “naive” algorithm that uses [ARPACK](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.svds.html)

    • Latent Dirichlet Allocation (LDA) [:page_facing_up:](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf)

      • lda - Python implementation using collapsed Gibbs sampling which follows scikit-learn interface [:page_facing_up:](https://www.pnas.org/content/pnas/101/suppl_1/5228.full.pdf)
      • PartiallyCollapsedLDA - Various fast parallelized samplers for LDA, including Partially Collapsed LDA, LightLDA, Partially Collapsed Light LDA and a very efficient Polya-Urn LDA
      • topicmodel-lib - Cython library for online/streaming LDA (Online VB, Online CVB0, Online CGS, Online OPE, Online FW, Streaming VB, Streaming OPE, Streaming FW, ML-OPE, ML-CGS, ML-FW)
      • jsLDA - JavaScript implementation of LDA topic modeling in the browser
      • lda-nodejs - Node.js implementation of LDA topic modeling
      • lda-purescript - PureScript, browser-based implementation of LDA topic modeling
      • TopicModels.jl - Julia implementation of LDA
      • turicreate - C++ [LDA](https://github.com/apple/turicreate/blob/master/userguide/text/README.md) and [aliasLDA](https://apple.github.io/turicreate/docs/api/generated/turicreate.topic_model.create.html) implementation with export to Apple's Core ML for use in iOS, macOS, watchOS, and tvOS apps
      • MeTA - C++ implementation of (parallel) collapsed [Gibbs sampling, CVB0 and SCVB](https://meta-toolkit.org/topic-models-tutorial.html)
      • Fugue - Java implementation of collapsed Gibbs sampling with slice sampling for hyper-parameter optimization
      • GA-LDA - R scripts using Genetic Algorithms (GA) for hyper-paramenter optimization, based on Panichella [:page_facing_up:](https://doi.org/10.1016/j.infsof.2020.106411)
      • Search-Based-LDA - R scripts using Genetic Algorithms (GA) for hyper-paramenter optimization by Panichella [:page_facing_up:](https://doi.org/10.1016/j.infsof.2020.106411)
      • Dodge - Python tuning tool that ignores redundant tunings [:page_facing_up:](https://arxiv.org/pdf/1902.01838.pdf)
      • LDADE - Python tuning tool using differential evolution [:page_facing_up:](https://arxiv.org/pdf/1608.08176.pdf)
      • ldatuning - R package to find optimal number of topics for LDA [:page_facing_up:](https://rpubs.com/siri/ldatuning)
      • topic_interpretability - Computation of the semantic interpretability of topics produced by topic models [:page_facing_up:](https://aclanthology.org/E14-1056.pdf)
      • topic-coherence-sensitivity - Code to compute topic coherence for several topic cardinalities and aggregate scores across them [:page_facing_up:](https://aclanthology.org/N16-1057.pdf)
      • topic-model-diversity - A collection of topic diversity measures for topic modeling [:page_facing_up:](https://dl.acm.org/doi/abs/10.1007/978-3-030-80599-9_4)
      • FastLDA - C++ implementation of LDA [:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/1401890.1401960)
      • dmlc - Single-and multi-threaded C++ implementations of [lightLDA](https://arxiv.org/pdf/1412.1576.pdf), [F+LDA](https://arxiv.org/pdf/1412.4986v1.pdf), [AliasLDA](https://dl.acm.org/doi/pdf/10.1145/2623330.2623756), forestLDA and many more
      • warpLDA - C++ cache efficient LDA implementation which samples each token in O(1) [:page_facing_up:](https://arxiv.org/pdf/1510.08628.pdf)
      • lightLDA - C++ implementation using O(1) Metropolis-Hastings sampling [:page_facing_up:](https://arxiv.org/pdf/1412.1576.pdf)
      • AliasLDA - C++ implemenation using Metropolis-Hastings and *alias* method[:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/2623330.2623756)
      • Yahoo-LDA - Yahoo!'s topic modelling framework [:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/2124295.2124312)
      • PLDA+ - Google's C++ implementation using data placement and pipeline processing [:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/1961189.1961198)
      • Familia - A toolkit for industrial topic modeling (LDA, SentenceLDA and Topical Word Embedding) [:warning:](https://github.com/baidu/Familia/issues/111) [:page_facing_up:](https://arxiv.org/pdf/1707.09823.pdf)
      • scikit-learn - Python implementation using online variational Bayes inference [:page_facing_up:](https://proceedings.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf)
      • lda-gensim - Python implementation using online variational inference [:page_facing_up:](https://proceedings.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf)
      • ldamulticore-gensim - Parallelized Python implementation using online variational inference [:page_facing_up:](https://proceedings.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf)
      • CVBLDA-TopicModel4J - Java implementation using collapsed variational Bayesian (CVB) inference [:page_facing_up:](https://papers.nips.cc/paper/2006/file/532b7cbe070a3579f424988a040752f2-Paper.pdf)
      • Vowpal Wabbit - C++ implementaion using online variational Bayes inference [:page_facing_up:](https://proceedings.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf)
      • Scalable - Scalable Hyperparameter Selection for LDA [:page_facing_up:](https://www.tandfonline.com/doi/full/10.1080/10618600.2020.1741378)
      • LDA\* - Tencent's hybrid sampler that uses different samplers for different types of documents in combination with an asymmetric parameter server [:page_facing_up:](http://www.vldb.org/pvldb/vol10/p1406-yu.pdf)
      • F+LDA - C++ implementation of F+LDA using an appropriately modified Fenwick tree [:page_facing_up:](https://arxiv.org/pdf/1412.4986v1.pdf)
      • GS-LDA-BIDMach - CPU and GPU-accelerated Scala implementation using Gibbs sampling
      • VB-LDA-BIDMach - CPU and GPU-accelerated Scala implementation using online variational Bayes inference
      • SparseLDA - Java algorithm and data structure for evaluating Gibbs sampling distributions used in Mallet [:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/1557019.1557121)
      • SaberLDA - GPU-based system that implements a sparsity-aware algorithm to achieve sublinear time complexity

    • Hierarchical Dirichlet Process (HDP) [:page_facing_up:](https://papers.nips.cc/paper/2004/file/fb4ab556bc42d6f0ee0f9e24ec4d1af0-Paper.pdf)

      • hca - C implementation using Gibbs sampling with/without burstiness modelling
      • bnp - Cython reimplementation based on *online-hdp* following scikit-learn's API.
      • tomotopy - Python extension for C++ implementation using Gibbs sampling [:page_facing_up:](https://www.jmlr.org/papers/volume10/newman09a/newman09a.pdf)
      • Mallet - Java-based package for topic modeling using Gibbs sampling
      • TopicModel4J - Java implementation using Gibbs sampling based on Chinese restaurant franchise metaphor
      • gensim - Python implementation using online variational inference [:page_facing_up:](http://proceedings.mlr.press/v15/wang11a/wang11a.pdf)
      • Scalable HDP - interesting paper

    • Hierarchical LDA (hLDA) [:page_facing_up:](https://dl.acm.org/doi/10.5555/2981345.2981348)

      • hlda - Python package based on *Mallet's* Gibbs sampler having a fixed depth on the nCRP tree
      • Mallet - Java implementation using Gibbs sampling

    • Dynamic Topic Model (DTM) [:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/1143844.1143859)

      • FastDTM - Scalable C++ implementation using Gibbs sampling with Stochastic Gradient Langevin Dynamics (MCMC-based) [:page_facing_up:](https://arxiv.org/pdf/1602.06049.pdf)
      • ldaseqmodel-gensim - Python implementation using online variational inference [:page_facing_up:](https://proceedings.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf)
      • tca - C implementation using Gibbs sampling with/without burstiness modelling [:page_facing_up:](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.705.1649&rep=rep1&type=pdf)

    • Miscellaneous topic models

      • BigTopicModel - C++ engine for running large-scale MedLDA models [:page_facing_up:](https://dl.acm.org/doi/10.1145/2487575.2487658)
      • YWWTools - Java-based package for various topic models by Weiwei Yang
      • trLDA - Python implementation of streaming LDA based on trust-regions [:page_facing_up:](http://proceedings.mlr.press/v37/theis15.pdf)
      • Logistic LDA - Tensorflow implementation of Discriminative Topic Modeling with Logistic LDA [:page_facing_up:](https://proceedings.neurips.cc/paper/2019/file/54ebdfbbfe6c31c39aaba9a1ee83860a-Paper.pdf)
      • EnsTop - Python implementation of *ENS*emble *TOP*ic modelling with pLSA
      • discLDA - C++ implementation of discLDA based on GibbsLDA++ [:page_facing_up:](https://papers.nips.cc/paper/2008/file/7b13b2203029ed80337f27127a9f1d28-Paper.pdf)
      • GuidedLDA - Python implementation that can be guided by setting some seed words per topic (using Gibbs sampling) [:page_facing_up:](https://www.aclweb.org/anthology/E12-1021.pdf)
      • seededLDA - R package that implements seeded-LDA for semi-supervised topic modeling
      • keyATM - R package for Keyword Assisted Topic Models.
      • BayesPA - Python interface for streaming implementation of MedLDA, maximum entropy discrimination LDA (max-margin supervised topic model) [:page_facing_up:](http://proceedings.mlr.press/v32/shi14.pdf)
      • DAPPER - Python implementation of Dynamic Author Persona (DAP) topic model [:page_facing_up:](https://arxiv.org/pdf/1811.01931.pdf)
      • ToT - Python implementation of Topics Over Time (A Non-Markov Continuous-Time Model of Topical Trends) [:page_facing_up:](https://dl.acm.org/doi/10.1145/1150402.1150450)
      • MLTM - C implementation of multilabel topic model (MLTM) [:page_facing_up:](https://www.mitpressjournals.org/doi/pdf/10.1162/NECO_a_00939)
      • Entropy-Based Topic Modeling - Java implementation of Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections
      • Dual-Sparse Topic Model - implemented in TopicModel4J using collapsed variational Bayes inference [:page_facing_up:](https://dl.acm.org/doi/10.1145/2566486.2567980)
      • sailing-pmls - Parallel LDA and medLDA implementation
      • sequence-models - Java implementation of block HMM and the mixed membership Markov model (M4)

    • Embedding based Topic Models

      • D-ETM - Dynamic Embedded Topic Model [:page_facing_up:](https://arxiv.org/pdf/1907.05545.pdf)
      • BERTopic - BERTopic supports guided, (semi-) supervised, and dynamic topic modeling and visualization [:page_facing_up:](https://arxiv.org/pdf/2203.05794.pdf)
      • CTM - CTMs combine contextualized embeddings (e.g., BERT) with topic models
      • ETM - Embedded Topic Model [:page_facing_up:](https://arxiv.org/pdf/1907.04907.pdf)
      • ProdLDA - Original TensorFlow implementation of Autoencoding Variational Inference (AEVI) for Topic Models [:page_facing_up:](https://arxiv.org/pdf/1703.01488.pdf)
      • pytorch-ProdLDA - PyTorch implementation of ProdLDA [:page_facing_up:](https://arxiv.org/pdf/1703.01488.pdf)
      • CatE - Discriminative Topic Mining via Category-Name Guided Text Embedding [:page_facing_up:](https://arxiv.org/pdf/1908.07162.pdf)
      • Top2Vec - Python implementation that learns jointly embedded topic, document and word vectors [:page_facing_up:](https://arxiv.org/pdf/2008.09470.pdf)
      • G-LDA - Java implementation of Gaussian LDA using word embeddings [:page_facing_up:](https://www.aclweb.org/anthology/P15-1077.pdf)
      • MetaLDA - Java implementation using Gibbs sampling that leverages document metadata and word embeddings [:page_facing_up:](https://arxiv.org/pdf/1709.06365.pdf)
      • LFTM - Java implementation of latent feature topic models (improving LDA and DMM with word embeddings) [:page_facing_up:](https://www.aclweb.org/anthology/Q15-1022.pdf)
      • CorEx - Recover latent factors with Correlation Explanation (CorEx) [:page_facing_up:](https://arxiv.org/pdf/1406.1222.pdf)
      • Anchored CorEx - Hierarchical Topic Modeling with Minimal Domain Knowledge [:page_facing_up:](https://arxiv.org/pdf/1611.10277.pdf)
      • Linear CorEx - Latent Factor Models Based on Linear Total CorEx [:page_facing_up:](https://arxiv.org/pdf/1706.03353v3.pdf)
      • lda2vec - Mixing dirichlet topic models and word embeddings to make lda2vec [:page_facing_up:](https://arxiv.org/pdf/1605.02019.pdf)
      • lda2vec-pytorch - PyTorch implementation of lda2vec
      • MG-LDA - Python implementation of (Multi-lingual) Gaussian LDA [:page_facing_up:](https://raw.githubusercontent.com/EliasKB/Multilingual-Gaussian-Latent-Dirichlet-Allocation-MGLDA/master/MGLDA.pdf)

    • Labeled Latent Dirichlet Allocation (LLDA, Labeled-LDA, L-LDA) [:page_facing_up:](https://www.aclweb.org/anthology/D09-1026.pdf)

      • topbox - Python wrapper for labeled LDA implementation of *Stanford TMT*
      • Labeled-LDA-Python - Python implementation (easy to use, does not scale)
      • JGibbLabeledLDA - Java implementation based on the popular [JGibbLDA](jgibblda.sourceforge.net) package
      • Mallet - Java implementation using Gibbs sampling [:page_facing_up:](http://www.mimno.org/articles/labelsandpatterns)
      • gensims_mallet_wrapper - Python wrapper for Mallet using gensim interface

    • Supervised LDA (sLDA) [:page_facing_up:](https://papers.nips.cc/paper/2007/file/d56b9fc4b0f1be8871f5e1c40c0067e7-Paper.pdf)

      • slda - Cython implementation of Gibbs sampling for LDA and various sLDA variants

    • Exotic models

      • PTM - Prescription Topic Model for Traditional Chinese Medicine Prescriptions [:page_facing_up:](https://ieeexplore.ieee.org/abstract/document/8242679) (interesting benchmark models)
      • TEM - Topic Expertise Model [:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/2505515.2505720)
      • KGE-LDA - Knowledge Graph Embedding LDA [:page_facing_up:](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewFile/14170/14086)
      • LDA-SP - A Latent Dirichlet Allocation Method for Selectional Preferences [:page_facing_up:](https://www.aclweb.org/anthology/P10-1044.pdf)
      • LDA+FFT - LDA and FFTs (Fast and Frugal Trees) for better comprehensibility [:page_facing_up:](https://arxiv.org/pdf/1804.10657.pdf)

    • Relational Topic Model (RTM)

      • Constrained-RTM - Java implementation of Contrained RTM [:page_facing_up:](https://doi.org/10.1016/j.ins.2019.09.039)

    • Non-Negative Matrix Factorization (NMF or NNMF)

      • scikit-learn - Python implementation using a [coordinate descent](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.214.6398&rep=rep1&type=pdf) or a [multiplicative update](https://arxiv.org/pdf/1010.1763.pdf) solver
      • gensim - Python implementation of [online NMF](https://arxiv.org/pdf/1604.02634.pdf)
      • BIDMach - CPU and GPU-accelerated Scala implementation with L2 loss

    • Author-topic Model (ATM) [:page_facing_up:](https://arxiv.org/pdf/1207.4169.pdf)

      • gensim - Python implementation with online training (constant in memory w.r.t. the number of documents)

  • Libraries & Toolkits

    • scikit-learn - Python library for machine learning ![GitHub Repo stars](https://img.shields.io/github/stars/scikit-learn/scikit-learn?style=social)
    • OCTIS - Python package to integrate, optimize and evaluate topic models ![GitHub Repo stars](https://img.shields.io/github/stars/MIND-Lab/OCTIS?style=social)
    • tmtoolkit - Python topic modeling toolkit with parallel processing power ![GitHub Repo stars](https://img.shields.io/github/stars/WZBSocialScienceCenter/tmtoolkit?style=social)
    • BIDMach - CPU and GPU-accelerated machine learning library ![GitHub Repo stars](https://img.shields.io/github/stars/BIDData/BIDMach?style=social)
    • BigARTM - Fast topic modeling platform ![GitHub Repo stars](https://img.shields.io/github/stars/bigartm/bigartm?style=social)
    • TopicNet - A high-level Python interface for BigARTM library ![GitHub Repo stars](https://img.shields.io/github/stars/machine-intelligence-laboratory/TopicNet?style=social)
    • RMallet - R package to interface with the Java machine learning tool MALLET ![GitHub Repo stars](https://img.shields.io/github/stars/mimno/RMallet?style=social)
    • R-lda - R package for topic modelling (LDA, sLDA, corrLDA, etc.) ![GitHub Repo stars](https://img.shields.io/github/stars/slycoder/R-lda?style=social)
    • topicmodels - R package with interface to C code for LDA and CTM ![GitHub Repo stars](https://img.shields.io/github/stars/cran/topicmodels?style=social)
    • lda++ - C++ library for LDA and (fast) supervised LDA (sLDA/fsLDA) using variational inference ![GitHub Repo stars](https://img.shields.io/github/stars/angeloskath/supervised-lda?style=social)
    • gensim - Python library for topic modelling ![GitHub Repo stars](https://img.shields.io/github/stars/RaRe-Technologies/gensim?style=social)
    • tomoto - Ruby extension for Gibbs sampling based *tomoto* which is written in C++ ![GitHub Repo stars](https://img.shields.io/github/stars/ankane/tomoto?style=social)
    • stm - R package for the Structural Topic Model ![GitHub Repo stars](https://img.shields.io/github/stars/bstewart/stm?style=social)

  • Research Implementations

    • Embedding based Topic Models

      • hLDA - C implementation of hierarchical LDA by David Blei
      • ctm-c - C implementation of the correlated topic model by David Blei
      • sLDA - C++ implementation of supervised topic models with a categorical response.
      • lda-c - C implementation using variational EM by David Blei
      • onlineldavb - Python online variational Bayes implementation by Matthew Hoffman [:page_facing_up:](https://proceedings.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf)
      • HDP - C++ implementation of hierarchical Dirichlet processes by Chong Wang
      • online-hdp - Python implementation of online hierarchical Dirichlet processes by Chong Wang
      • ctr - C++ implementation of collaborative topic models by Chong Wang
      • dtm - C implementation of dynamic topic models by David Blei & Sean Gerrish
      • diln - C implementation of Discrete Infinite Logistic Normal (with HDP option) by John Paisley
      • turbotopics - Python implementation that finds significant multiword phrases in topics by David Blei
      • LDAGibbs - Java implementation of LDA using Gibbs sampling by Liu Yang
      • cvbLDA - Python C extension implementation of collapsed variational Bayesian inference for LDA
      • fast - A Fast And Scalable Topic-Modeling Toolbox (Fast-LDA, CVB0) by Arthur Asuncion and colleagues [:page_facing_up:](https://arxiv.org/pdf/1205.2662.pdf)

    • Embedding based Topic Models

      • Matlab Topic Modeling Toolbox - Matlab implementations of LDA, ATM, HMM-LDA, LDA-COL (Collocation) models by Mark Steyvers and Tom Griffiths
      • :fork_and_knife:
      • Mr.LDA - Scalable Topic Modeling using Variational Inference in MapReduce [:page_facing_up:](https://dl.acm.org/doi/10.1145/2187836.2187955)
      • GibbsLDA++ - C++ implementation using Gibbs sampling [:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/1367497.1367510)
      • JGibbLDA - Java implementation using Gibbs sampling
      • Stanford Topic Modeling Toolbox - Scala implementation of LDA, labeledLDA, PLDA, PLDP by Daniel Ramage and Evan Rosen

  • Learning Implementations (hopefully easy to understand)

    • Embedding based Topic Models

      • Topic-Model - Python implementation of LDA, Labeled LDA, ATM, Temporal Author-Topic Model using Gibbs sampling
      • topic_models - Python implementation of LSA, PLSA and LDA

  • Probabilistic Programming Languages (PPL) (a.k.a. Build your own Topic Model)

    • Embedding based Topic Models

      • Stan - Platform for statistical modeling and high-performance statistical computation, e.g., [LDA](https://mc-stan.org/docs/2_26/stan-users-guide/latent-dirichlet-allocation.html) [:page_facing_up:](https://files.eric.ed.gov/fulltext/ED590311.pdf)
      • Turing.jl - Julia library for general-purpose probabilistic programming [:page_facing_up:](http://proceedings.mlr.press/v84/ge18b/ge18b.pdf)
      • TFP - Probabilistic reasoning and statistical analysis in TensorFlow, e.g., [LDA](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py) [:page_facing_up:](https://arxiv.org/pdf/2001.11819.pdf)
      • edward2 - Simple PPL with core utilities in the NumPy and TensorFlow ecosystem [:page_facing_up:](https://arxiv.org/pdf/1811.02091.pdf)
      • pyro - PPL built on PyTorch, e.g., [prodLDA](http://pyro.ai/examples/prodlda.html) [:page_facing_up:](https://www.jmlr.org/papers/volume20/18-403/18-403.pdf)
      • edward - A PPL built on TensorFlow, e.g., [LDA](http://edwardlib.org/iclr2017?Figure%2011.%20Latent%20Dirichlet%20allocation) [:page_facing_up:](https://arxiv.org/pdf/1610.09787.pdf)
      • ZhuSuan - A PPL for Bayesian deep learning, generative models, built on Tensorflow, e.g., [LDA](https://zhusuan.readthedocs.io/en/latest/tutorials/lntm.html) [:page_facing_up:](https://arxiv.org/pdf/1709.05870.pdf)

  • Visualizations

    • Embedding based Topic Models

      • LDAvis - R package for interactive topic model visualization
      • pyLDAvis - Python library for interactive topic model visualization
      • scalaLDAvis - Scala port of pyLDAvis
      • dtmvisual - Python package for visualizing DTM (trained with gensim)
      • TMVE online - Online Django variant of topic model visualization engine (*TMVE*)
      • TMVE - Original topic model visualization engine (LDA trained with *lda-c*) [:page_facing_up:](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/viewFile/4645/5021)
      • wordcloud - Python package for visualizing topics via word_cloud
      • Mallet-GUI - GUI for creating and analyzing topic models produced by MALLET
      • TWiC - Topic Words in Context is a highly-interactive, browser-based visualization for MALLET topic models
      • dfr-browser - Explore Mallet's topic models of texts in a web browser
      • Termite - Explore topic models using term-topic matrix, group-in-a-box visualization or scatter plot.
      • Topics - Python library for topic modeling and visualization
      • TopicsExplorer - Explore your own text collection with a topic model – without prior knowledge [:page_facing_up:](https://dh2018.adho.org/a-graphical-user-interface-for-lda-topic-modeling)
      • topicApp - A Simple Shiny App for Topic Modeling
      • stminsights - A Shiny Application for Inspecting Structural Topic Models
      • topicmodel-lib - Python wrapper for TMVE for visualizing LDA (trained with topicmodel-lib)

  • Dirichlet hyperparameter optimization techniques

  • Resources

    • Embedding based Topic Models

      • David Blei - David Blei's Homepage with introductory materials
Programming Languages
Python 50 C++ 26 Java 20 C 12 R 9 JavaScript 7 Jupyter Notebook 6 Julia 2 MATLAB 1 Matlab 1
Categories
Models 137 Visualizations 16 Research Implementations 14 Libraries & Toolkits 13 Dirichlet hyperparameter optimization techniques 8 Probabilistic Programming Languages (PPL) (a.k.a. Build your own Topic Model) 7 Popular Implementations (but not maintained anymore) 6 Related awesome lists 3 Learning Implementations (hopefully easy to understand) 2 Resources 1 License 1
Sub Categories
Embedding based Topic Models 75 Latent Dirichlet Allocation (LDA) [:page_facing_up:](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) 38 Topic Models for short documents 31 Miscellaneous topic models 17 Hierarchical Dirichlet Process (HDP) [:page_facing_up:](https://papers.nips.cc/paper/2004/file/fb4ab556bc42d6f0ee0f9e24ec4d1af0-Paper.pdf) 7 Truncated Singular Value Decomposition (SVD) / Latent Semantic Analysis (LSA) / Latent Semantic Indexing (LSI) 6 Exotic models 5 Labeled Latent Dirichlet Allocation (LLDA, Labeled-LDA, L-LDA) [:page_facing_up:](https://www.aclweb.org/anthology/D09-1026.pdf) 5 Dynamic Topic Model (DTM) [:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/1143844.1143859) 3 Non-Negative Matrix Factorization (NMF or NNMF) 3 Hierarchical LDA (hLDA) [:page_facing_up:](https://dl.acm.org/doi/10.5555/2981345.2981348) 2 Author-topic Model (ATM) [:page_facing_up:](https://arxiv.org/pdf/1207.4169.pdf) 1 Supervised LDA (sLDA) [:page_facing_up:](https://papers.nips.cc/paper/2007/file/d56b9fc4b0f1be8871f5e1c40c0067e7-Paper.pdf) 1 Relational Topic Model (RTM) 1
Keywords
topic-modeling 47 machine-learning 28 lda 17 python 15 nlp 13 data-science 11 natural-language-processing 10 topic-models 9 deep-learning 8 latent-dirichlet-allocation 8 text-mining 7 probabilistic-programming 6 word-embeddings 5 gibbs-sampling 5 r 5 statistics 5 tensorflow 4 bayesian-methods 4 bayesian-inference 4 short-text 4 bayesian 4 visualization 4 digital-humanities 4 variational-inference 3 bert 3 neural-networks 3 information-theory 3 unsupervised-learning 3 data-analysis 3 hyperparameter-optimization 3 hyperparameter-tuning 3 topic-modelling 3 clustering 3 text-analytics 2 evaluation-metrics 2 mallet 2 java 2 text-classification 2 text-analysis 2 topic 2 neural-topic-models 2 information-retrieval 2 embeddings 2 graphical-models 2 nlp-library 2 classification 2 c-plus-plus 2 python-library 2 neural-network 2 nlp-machine-learning 2