Topica: fast, all-purpose topic modeling for Python

PyPI CI Docs Website License: Apache-2.0

topica is a fast, all-purpose topic-modeling library for Python, built for computational social scientists who want to go from a column of text to publishable results in one workflow. It brings together models usually split across JVM tools like MALLET and R packages like stm, more than forty in all (LDA, STM, CTM, plus neural, dynamic, and embedding-based models), each paired with the validation, covariate-effect, and reporting tools reviewers expect. Where general toolkits like Gensim or BERTopic give you topics, topica is built around the question social scientists ask of them next: how topic prevalence and content relate to covariates, with reference-validated models and reproducible fits. It installs as a single wheel that needs only NumPy and pandas: no JVM, no PyTorch.

pip install topica

Quick start

Point topica at a DataFrame and read the topics. This runs exactly as written, on a bundled example dataset, right after install:

import topica

df = topica.datasets.load_gadarian()          # bundled; loads offline
corpus = topica.from_dataframe(
    df, text_col="open.ended.response", stopwords=topica.ENGLISH_STOPWORDS
)

model = topica.LDA(num_topics=5, seed=42)
model.fit(corpus)                             # sensible defaults; no tuning required
print(topica.summary(model))                  # top words per topic

from_dataframe keeps your metadata aligned to the documents that survive pruning, so the same corpus feeds a structural topic model that relates topic prevalence to a covariate, with a well-calibrated hypothesis test:

prevalence = corpus.metadata[["treatment"]]   # a numeric DataFrame goes straight in

stm = topica.STM(num_topics=5, seed=42)
stm.fit(corpus, prevalence, prevalence_names=["treatment"])

draws  = topica.posterior_theta_samples(stm, nsims=30, seed=0)
effect = topica.estimate_effect(draws, prevalence, feature_names=["treatment"])

Your own data is one line away: pass pandas.read_csv("yours.csv") to from_dataframe. See the getting-started guide and the worked examples for analyses end to end.

Fits are reproducible and validated: the variational models are identical to the bit, the samplers reproduce from a fixed seed and thread count, and every model is checked against its reference implementation (R stm, MALLET, keyATM, and more).

The core needs only NumPy and pandas. Optional extras add features without weighing it down: topica[viz] (matplotlib plots), topica[formula] (R-style formulas), topica[polars] (Polars frames), and topica[llm] (LLM labels and embeddings, OpenAI or local via ollama).

Models

Starting out? LDA for general topics, STM to relate topics to covariates, HDP to let the data choose the number of topics, and BERTopic or CombinedTM for embedding-based topics. The full roster follows.

All models (more than forty, grouped by what you bring and what you want; click to expand)

Models are organized by what you bring and what you want, not by inference family. The from topica import X namespace is flat; topica.list_models(group=…, brings=…, inference=…, determinism=…) filters this roster in code. Brings is what you supply beyond raw text; Reproducibility is bit-exact (identical regardless of thread count), seed-reproducible (identical from a fixed seed and thread count), or llm-bounded.

General-purpose

Model Brings Inference Reproducibility Summary
LDA text gibbs seed-reproducible Classic latent Dirichlet allocation via a fast SparseLDA collapsed-Gibbs sampler.
OnlineLDA text variational seed-reproducible Online (streaming) variational-Bayes LDA (Hoffman et al. 2010): minibatch stochastic VB with a decaying learning rate and a streaming partial_fit; the gensim LdaModel analogue for very large or streaming corpora.
CTM text variational bit-exact Correlated topic model: a logistic-normal prior that lets topics co-occur.
ProdLDA text vae seed-reproducible Product-of-experts LDA (AVITM) for sharper, more coherent topics; hand-coded VAE.
HDP text gibbs seed-reproducible Hierarchical Dirichlet process: infers the number of topics from the data.
NMF text matrix-factorization bit-exact Non-negative matrix factorization of the document-term matrix via multiplicative updates.
LSA text svd seed-reproducible Latent semantic analysis: a truncated SVD of the weighted document-term matrix.
AnchorLDA text matrix-factorization bit-exact Anchor-words spectral recovery (Arora et al. 2013): deterministic, Gibbs-free topics from the word co-occurrence matrix.
PolylingualLDA text gibbs seed-reproducible Polylingual topic model (Mimno et al. 2009): aligned topics across languages from document tuples that share one topic distribution.

Covariates & structure

Model Brings Inference Reproducibility Summary
STM text, metadata variational bit-exact Structural topic model: relate topic prevalence and content to covariates.
STS text, metadata variational bit-exact Structural topic-and-sentiment model over document metadata.
SAGE text, metadata gibbs seed-reproducible Sparse additive generative model: the same topic worded differently across groups.
DMR text, metadata gibbs seed-reproducible Dirichlet-multinomial regression: a document-metadata prior on topic proportions.
GDMR text, metadata gibbs seed-reproducible Generalized DMR with a smooth (Legendre-basis) prior over continuous covariates.
Scholar text, metadata, labels vae seed-reproducible SCHOLAR (Card et al. 2018): a ProdLDA VAE with a covariate-shifted prevalence prior, an optional supervised label head, and optional content (topic-covariate) word deviations — neural STM prevalence + sLDA + SAGE.
RTM text, links variational seed-reproducible Relational topic model (Chang & Blei 2010): jointly models document text and a link graph (citations, hyperlinks, adjacency); predicts links from words and words from links.
FactorialLDA text gibbs seed-reproducible Factorial LDA (Paul & Dredze 2012): each token is a K-tuple of latent factors (e.g. topic x sentiment); structured word priors tie tuples sharing a component and a sparsity prior deactivates unsupported tuples.

Guided & supervised

Model Brings Inference Reproducibility Summary
KeyATM text, seeds gibbs seed-reproducible Keyword-assisted topics: anchor named topics with a few seed words each.
SeededLDA text, seeds gibbs seed-reproducible Seeded LDA: steer named topics toward supplied seed words.
LabeledLDA text, labels gibbs seed-reproducible Labeled LDA: each document label is a topic; tokens are restricted to its labels.
SupervisedLDA text, labels variational seed-reproducible Supervised LDA: topics shaped to predict a per-document real-valued response.
DiscLDA text, labels gibbs seed-reproducible Discriminative LDA (Lacoste-Julien et al. 2008): topics split into per-class and shared blocks; reads how classes talk differently.

Short text

Model Brings Inference Reproducibility Summary
GSDMM text gibbs seed-reproducible Gibbs-sampling Dirichlet mixture: one topic per short document.
PT text gibbs seed-reproducible Pseudo-document topic model: pool short texts into pseudo-documents.
BTM text gibbs seed-reproducible Biterm topic model: learns topics from corpus-level word co-occurrence (biterms).

Dynamic & hierarchical

Model Brings Inference Reproducibility Summary
DTM text, times variational seed-reproducible Dynamic topic model: a fixed topic set whose word distributions drift across time slices.
DETM text, embeddings, times vae seed-reproducible Dynamic embedded topic model: embedding-factored topics that drift across time slices, fit as an amortized VAE.
HLDA text gibbs seed-reproducible Hierarchical LDA (nested CRP): a learned tree of super- and sub-topics.
PA text gibbs seed-reproducible Pachinko allocation: a DAG of super- and sub-topics.

Embedding-based

Model Brings Inference Reproducibility Summary
BERTopic text, embeddings clustering seed-reproducible Cluster document embeddings; label topics by class-based TF-IDF.
Top2Vec text, embeddings clustering seed-reproducible Topics as dense regions in a joint document-word embedding space.
SemanticSignalSeparation text, embeddings ica seed-reproducible Topics as independent axes of semantic space (S3, Kardos et al. 2025): FastICA over the document embeddings, with each word's importance read off by projecting the vocabulary embeddings onto each axis. Signed poles.
ETM text, embeddings variational seed-reproducible Embedded topic model: topic-word distributions factored through word embeddings.
FASTopic text, embeddings optimal-transport seed-reproducible Topics from optimal-transport plans between document, topic, and word embeddings.
EmbeddingLDA text, embeddings, seeds gibbs seed-reproducible Seeded LDA whose seed sets are expanded with nearest neighbors in an embedding space.
CombinedTM text, embeddings vae seed-reproducible Contextualized ProdLDA: encoder reads the bag of words plus a document embedding.
ZeroShotTM text, embeddings vae seed-reproducible Contextualized ProdLDA: encoder reads the document embedding alone, enabling cross-lingual transfer.
InfoCTM text, dictionary vae seed-reproducible Cross-lingual: two ProdLDA models aligned by a bilingual dictionary through a mutual-information term.

Ideal point

Model Brings Inference Reproducibility Summary
Wordfish text em bit-exact Poisson scaling (Slapin & Proksch 2008): an unsupervised one-dimensional ideal-point estimate from word frequencies alone, no topics. The word-frequency baseline companion to IdealPointTM.
TBIP text variational seed-reproducible Text-Based Ideal Points (Vafa, Naidu & Blei 2020): a Poisson factorization whose neutral topic-word intensities are rescaled by a per-word ideological factor exp(x_s * eta_kv), with the author position x_s latent. Fit by the paper's mean-field variational inference (reparameterized SVI). Recovers ideological scales from unlabeled text.
PartyEmbeddings text, metadata neural-embedding seed-reproducible Party embeddings (Rheault & Cochrane 2020): a PV-DM paragraph-vector model trained by negative sampling with party-period metadata tags; the leading principal components of the learned party vectors give the ideological scale, and words share the space so a party's language can be read off by proximity. The corpus-trained word-embedding member of the ideal-point family.

LLM-based

Model Brings Inference Reproducibility Summary
TopicGPT text, llm prompting llm-bounded LLM-driven topic discovery: prompt a model to propose, refine, and assign a topic taxonomy with descriptions.

Experimental

Shipped before a published paper and reference-implementation parity (topica's bar for a validated model). Gated: call topica.enable_experimental() (or set TOPICA_EXPERIMENTAL=1) before use. These may change or be removed without a deprecation cycle.

Model Brings Inference Reproducibility Summary
TensorLDA text svd seed-reproducible Online Tensor LDA (Kangaslahti et al. 2026): deterministic method-of-moments topic modeling via second and third-order cumulants.
NarrativeTM text gibbs seed-reproducible Intra-document narrative trajectory model: captures how topic prevalence shifts across the progress of a text.
IdealPointTM text, embeddings variational seed-reproducible Topic model with a latent ideal-point head: each author gets a low-dimensional position that shifts within-topic word choice, with a per-topic discrimination. Consumes word tokens as counts (Wordfish with topics) or, when word embeddings are supplied to fit, factored through them as in ETM. The unsupervised, latent-trait twin of the STM content covariate.
IdealPointSentenceTM text, embeddings em seed-reproducible Continuous ideal-point topic model over sentence/document embeddings: topics are Gaussian clusters whose centroids are displaced by a latent author position. The sentence-embedding sibling of IdealPointTM, fit by EM.

Every model exposes the same shape: fit(docs, …), then topic_word (φ), doc_topic (θ), top_words(n), and save/load, so one diagnostic, labeling, and effect-estimation stack applies to all of them and a new model inherits it for free. The embedding-based models take document vectors from any embedder (sentence-transformers, an API, or a local model such as ollama; no PyTorch or UMAP/numba in the wheel). Full guides: the models and embedding topics.

Diagnostics & analysis

Model-agnostic: they work on any fitted model's topic_word/doc_topic:

  • Quality: coherence (u_mass, c_v, c_uci, c_npmi; co-occurrence counting in the Rust core), exclusivity, topic_diversity, quality_frontier
  • Labeling: label_topics (prob / FREX / lift / score), frex, relevance, find_thoughts, topic_table, summary
  • Validation: word_intrusion, document_intrusion, bootstrap_stability, search_k
  • Reliability: select_model (fit many seeds) and ensemble (combine runs into a consensus more reliable than any single fit — cluster/align/stable methods, the last a gensim EnsembleLda port)
  • Comparison: fighting_words (weighted log-odds) for contrasting corpora
  • Covariate effects: estimate_effect (method of composition, cluster-robust SEs, GLM links), topic_correlation, and the design helpers one_hot, spline, and interaction (all top level; they build covariate bases for any model's design matrix); posterior_theta_samples draws θ for the logistic-normal models (STM/CTM)
  • Preprocessing: tokenize, learn_phrases / apply_phrases, split_documents, the Corpus class

See diagnostics and covariate effects.

Performance

topica runs on a parallel Rust core. It is several times faster than R stm — the single-threaded field standard — for the structural and other variational models, and it matches the hand-tuned compiled samplers core for core: parity with Java MALLET on plain LDA and with the C++ keyATM on keyword models. Fit to convergence (both at the same emtol, spectral start), on real corpora:

Model Reference topica speedup (to convergence)
STM R stm 1.7–2.7× single-threaded, ~5–7× multicore
LDA Java MALLET parity single-threaded; multithread speedup grows with corpus size
keyATM R keyATM parity single-threaded, ~2× multithreaded

topica also fits in about a quarter of R stm's memory (≈180MB against ≈675MB at 5,000 documents). For the approximate parallel Gibbs samplers the multithreaded speedup grows with corpus size: the per-sweep count-table merge is fixed overhead, so larger corpora amortize it over more sampling work. LDA's eight-core speedup over MALLET runs about 3× at 2,000 documents and reaches ~4× at 5,000.

Every fit is reproducible from a fixed seed and validated against its reference. See Benchmarks for the full methodology; reproduce the structural-model table with python benchmarks/bench_stm_convergence.py and the size-varying LDA curve with python benchmarks/speed_vs_size.py.

Install from source

pip install maturin
git clone https://github.com/nealcaren/topica && cd topica
python -m venv .venv && source .venv/bin/activate
maturin develop --release --features python

Requires numpy >= 1.21. Use --release (the debug build is much slower).

Acknowledgements

Topica was inspired by a post from David Mimno about porting Java MALLET to Rust. As a long-time Python user, I had long been jealous of the topic-modeling tools available in other languages; this seemed like an opportunity to make those capabilities easier to use in Python for me and for others.

As such, Topica stands on a generation of open topic-modeling research and code. Each entry below lists the reference, its authors and year, and the topica class(es) it underlies; the other models are Rust ports or reimplementations, validated against these reference implementations.

  • MALLET (McCallum, 2002) — LDA, DMR, LabeledLDA: the SparseLDA sampler, Dirichlet-multinomial regression, and hyperparameter optimization. LDA began as a port of David Mimno's RustMallet (Apache-2.0) and follows its SparseLDA sampler and fixed-point optimizer closely, but uses its own RNG (PCG), so it is not byte-identical to RustMallet. Against Java MALLET (also a different RNG) it recovers the same topics on a planted corpus (cosine 1.000)
  • stm (Roberts, Stewart & Tingley, 2019) — STM, CTM, SAGE: variational EM, estimateEffect, searchK, FREX, spectral initialization, and the method of composition
  • sts (Chen & Mankad, 2024) — STS: the Structural Topic and Sentiment-Discourse model — the joint prevalence/sentiment Laplace E-step and the Poisson topic-word M-step, validated against the package
  • lda-c / ctm-c / dtm and hdp (Blei lab, 2006–2007) — CTM, DTM, HDP: the CTM, Dynamic Topic Model, and HDP samplers
  • gensim (Řehůřek & Sojka, 2010) — DTM, ensemble, OnlineLDA: the coherence-pipeline conventions (the coherence_type= API and default sliding windows; the measures themselves are Röder et al. 2015 and Mimno et al. 2011), the LdaSeqModel DTM reference, the EnsembleLda (CBDBSCAN stable-topic) method ported for ensemble(method="stable"), and the LdaModel online-VB reference for OnlineLDA
  • onlineldavb (Hoffman, Blei & Bach, 2010) — OnlineLDA: online (streaming) variational Bayes for LDA — the minibatch stochastic-VB E-step, the decaying Robbins-Monro learning rate, and the streaming partial_fit; written from the paper and validated against onlineldavb.py and gensim's LdaModel as external oracles (both are copyleft, so no code was copied)
  • tomotopy (bab2min, 2020) — API conventions (summary, the short-text models), and GDMR (generalized DMR; Lee & Song, 2020), validated against its GDMRModel
  • scikit-learn (Pedregosa et al., 2011) — NMF: the multiplicative-update solver (Lee & Seung, 2001) and the NNDSVD initialization (Boutsidis & Gallopoulos, 2008), validated against sklearn.decomposition.NMF (BSD-3-Clause); and LSA: latent semantic analysis / indexing (Deerwester et al., 1990), validated against sklearn.decomposition.TruncatedSVD (BSD-3-Clause) including its svd_flip sign convention. The numerics are reimplemented in Rust; the randomized truncated SVD shared by both (it seeds NMF's NNDSVD and is the LSA factorization itself) follows Halko et al. (2011).
  • keyATM (Eshima, Imai & Sasaki, 2024) — KeyATM: the base, covariate, and dynamic models, the information-theory token weighting, and the Chib (1998) change-point HMM, validated against the package
  • seededlda (Watanabe, 2023) — SeededLDA: the corpus-frequency-scaled seed prior (count × weight × 100), validated against the package's seed matrix and seeded topics
  • LightLDA (Yuan et al., 2015) — LDA: the alias-table Metropolis-Hastings sampler
  • GSDMM (Yin & Wang, 2014) — GSDMM: the movie-group-process mixture for short text
  • BTM (Yan, Guo, Lan & Cheng, 2013; R package by Jan Wijffels) — BTM: the biterm co-occurrence topic model for short text
  • Polylingual Topic Models (Mimno, Wallach, Naradowsky, Smith & McCallum, 2009) — PolylingualLDA: LDA over aligned document tuples that share one topic distribution, giving topics aligned across many languages; validated against MALLET's PolylingualTopicModel as a black-box oracle
  • DiscLDA (Lacoste-Julien, Sha & Jordan, 2008) — DiscLDA: discriminative LDA with per-class and shared topic blocks; the fixed block-transform variant, validated against the paper's 20 Newsgroups feature-classification result (no reference implementation exists, so it is paper-derived)
  • Factorial LDA (Paul & Dredze, 2012) — FactorialLDA: sparse multi-dimensional topics, where each token is a K-tuple of latent factors tied by structured log-linear priors; implemented from the paper's mathematics (the reference Java is GPL and non-reproducible, so the port is certified by finite-difference gradient and factor-tying tests plus planted recovery, not seed parity)
  • ProdLDA / AVITM (Srivastava & Sutton, 2017) — ProdLDA: autoencoding variational inference and the product-of-experts word model
  • SCHOLAR (Card, Tan & Smith, 2018; reference dallascard/scholar, Apache-2.0) — Scholar: metadata in a ProdLDA VAE — a covariate-dependent topic-prevalence prior (neural STM prevalence), an optional supervised label head (neural sLDA), and optional content/topic-covariate word deviations (neural SAGE), on topica's ProdLDA backbone, validated against the reference as a numerical oracle
  • BERTopic (Grootendorst, 2022) and Top2Vec (Angelov, 2020) — BERTopic, Top2Vec: the embedding-clustering pipeline, class-based TF-IDF, and the reduce → cluster → represent design
  • CETopic / topicx (Zhang, Fang, Chen & Namazi-Rad, NAACL 2022, MIT) — BERTopic(weighting="tfidf-idf"): the TFIDF×IDF_i topic-word selection scheme (a corpus-level TF-IDF averaged per cluster times a cross-cluster IDF penalty), ported faithfully to the reference's scikit-learn defaults
  • S³ / turftopic (Kardos, Kostkan, Enevoldsen, Vermillet, Nielbo & Rocca, ACL 2025, MIT) — SemanticSignalSeparation: Semantic Signal Separation, FastICA over contextual document embeddings with topic words read off by projecting the vocabulary embeddings onto each independent axis, ported faithfully to the reference's scikit-learn FastICA defaults
  • ETM (Dieng, Ruiz & Blei, 2020) — ETM: the Embedded Topic Model (per-document variational EM and an amortized VAE)
  • DETM (Dieng, Ruiz & Blei, 2019) — DETM: the Dynamic Embedded Topic Model (structured amortized variational inference with a hand-coded LSTM)
  • FASTopic (Wu et al., 2024) — FASTopic: the optimal-transport topic model
  • contextualized-topic-models (Bianchi et al., MIT) — CombinedTM (Bianchi, Terragni & Hovy, 2021) and ZeroShotTM (Bianchi, Nozza & Hovy, 2021): ProdLDA encoders that read a contextual document embedding, alongside or in place of the bag of words
  • quanteda.textmodels (Benoit et al., 2018) — Wordfish: the Slapin & Proksch (2008) Poisson scaling model, validated against its textmodel_wordfish (the recovered scale and the analytic position standard errors both match at correlation 1.00 on a corpus sampled from the model)
  • tbip (Vafa, Naidu & Blei, 2020) — TBIP: Text-Based Ideal Points; the official implementation is TensorFlow 1.x, so topica reimplements the published model and its mean-field variational inference, validated against an independent PyTorch reference
  • TensorLy TLDA (Kangaslahti, Ebanks, Kossaifi, Liu, Alvarez & Anandkumar, 2026) — TensorLDA: online tensor latent Dirichlet allocation via second- and third-order moments. The Rust implementation is experimental; see the TensorLDA validation record for its current evidence and limitations.
  • partyembed (Rheault & Cochrane, 2020) — PartyEmbeddings: party embeddings via a PV-DM paragraph-vector model with party-period metadata tags, placed by PCA of the learned party vectors. The reference builds on gensim's Doc2Vec; topica reimplements the PV-DM negative-sampling training in Rust (from Mikolov et al. 2013 and Le & Mikolov 2014) and is validated against that Doc2Vec scale (correlation 1.00 on a planted ordering)
  • CLNTM (Nguyen & Luu, 2021) — the InfoNCE contrastive regularization on topic vectors offered by the contrastive= flag on the VAE models
  • WHAI / Weibull-Dirichlet VAE (Zhang et al., 2018; Burkhardt & Kramer, 2019) — the Weibull-reparameterized Dirichlet prior offered by prior="dirichlet" on the VAE models
  • Neural variational topic models with alternative priors (Miao, Grefenstette & Blunsom, 2017; Nalisnick & Smyth, 2017) — the Gaussian stick-breaking prior offered by prior="stick_breaking" on the VAE models
  • TopicGPT (Pham et al., NAACL 2024, MIT) — TopicGPT: the generate / refine / assign prompt flow for LLM-driven topic discovery

The embedding-native models build on two pure-Rust crates: petal-clustering for HDBSCAN and umap-rs for the optional UMAP reducer, both BLAS-free.

Full citations for every model and reference implementation, and how to cite topica, are on the Citing page.

Contributing, tests, and support

Contributions are welcome. See CONTRIBUTING for the development setup and workflow, CONTRIBUTING-MODELS for adding a new topic model, and the conventions guide for the cross-model naming and API contract. All participants are expected to follow our Code of Conduct.

To run the test suite after a source build:

cargo test --lib                                   # Rust unit tests
python -m pytest tests/ -q                         # Python tests
mkdocs build --strict                              # docs build clean

Every push runs these on CI (see the badge above). The parity/ checks validate models against their reference implementations (R stm, keyATM, MALLET); they skip cleanly when those toolchains are not installed.

  • Report a bug or request a feature: open an issue.
  • Ask a question or share how you are using topica: start a discussion.

topica is maintained by Neal Caren. Issues and pull requests are triaged on a best-effort basis.

Citation

If you use topica in published work, please cite it. GitHub's Cite this repository button (top right) generates a formatted reference from CITATION.cff. A software paper is in preparation; until it appears, cite the software release:

@software{caren_topica,
  author  = {Caren, Neal},
  title   = {topica: fast, all-purpose topic modeling for Python},
  year    = {2026},
  url     = {https://github.com/nealcaren/topica},
  version = {0.54.0}
}

Replace version with the release you used. For the individual models and their reference implementations, see the Citing page.

License

Apache-2.0 — see LICENSE.

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Author: Neal Caren

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0.58.0

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0.57.0

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0.56.0

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This release

0.55.0 This release

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0.54.0

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0.7.1

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0.1.1

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0.1.0

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