{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-latent-dirichlet-allocation-lda","slug":"latent-dirichlet-allocation-lda","name":"Latent Dirichlet Allocation (LDA)","type":"product","url":"https://jmlr.csail.mit.edu/papers/v3/blei03a.html","page_url":"https://unfragile.ai/latent-dirichlet-allocation-lda","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-latent-dirichlet-allocation-lda__cap_0","uri":"capability://data.processing.analysis.probabilistic.topic.discovery.from.document.collections","name":"probabilistic-topic-discovery-from-document-collections","description":"Discovers latent topics in large document collections using a three-level hierarchical Bayesian model (documents → topics → words). Implements Gibbs sampling or variational inference to infer the posterior distribution over topic-document and topic-word assignments, enabling unsupervised extraction of semantic themes without manual labeling or predefined categories.","intents":["I need to automatically discover what topics are discussed across thousands of documents without manually categorizing them","I want to understand the semantic structure of a text corpus and identify dominant themes","I need to reduce high-dimensional bag-of-words representations into interpretable topic distributions"],"best_for":["data scientists analyzing large text corpora (news archives, research papers, social media)","information retrieval teams building topic-based search and recommendation systems","researchers in computational linguistics and digital humanities studying document collections"],"limitations":["Requires manual selection of topic count K — no automatic determination; wrong K severely degrades interpretability","Bag-of-words assumption ignores word order, syntax, and semantic relationships; struggles with short documents or sparse vocabularies","Gibbs sampling convergence can be slow on very large corpora (millions of documents); variational inference trades accuracy for speed","No built-in handling of polysemy or context-dependent word meanings; each word type has single topic distribution","Requires preprocessing: tokenization, stopword removal, and vocabulary curation; sensitive to these choices"],"requires":["Text corpus with minimum 100+ documents for meaningful topic discovery","Preprocessed token sequences (whitespace-separated or array format)","Computational resources: O(D*V*K) memory where D=documents, V=vocabulary size, K=topics","Python 3.6+ with NumPy/SciPy for reference implementations (gensim, scikit-learn)"],"input_types":["document collection (list of token sequences or raw text)","vocabulary mapping (word → integer ID)","hyperparameters: topic count K, Dirichlet priors α and β"],"output_types":["topic-word distributions (K × V matrix: P(word|topic))","document-topic distributions (D × K matrix: P(topic|document))","topic assignments per token (for Gibbs sampling trace)","log-likelihood scores for model evaluation"],"categories":["data-processing-analysis","unsupervised-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-latent-dirichlet-allocation-lda__cap_1","uri":"capability://data.processing.analysis.scalable.posterior.inference.via.variational.approximation","name":"scalable-posterior-inference-via-variational-approximation","description":"Approximates intractable posterior distributions using mean-field variational inference, decomposing the joint posterior into independent factors over topics and documents. Iteratively optimizes variational parameters (topic-document and topic-word Dirichlet parameters) to minimize KL divergence from true posterior, enabling inference on corpora with millions of documents where exact Gibbs sampling becomes prohibitively slow.","intents":["I need to fit LDA to a massive corpus (millions of documents) without waiting days for Gibbs sampling convergence","I want to estimate topic distributions for new documents without retraining the full model","I need principled uncertainty estimates over topic assignments, not just point estimates"],"best_for":["production systems requiring fast inference on large-scale document streams","researchers comparing multiple topic counts K and needing rapid model evaluation","applications requiring online/streaming topic inference with bounded latency"],"limitations":["Mean-field assumption (independence between latent variables) is often violated in practice; underestimates posterior variance","Convergence to local optima is common; sensitive to initialization of variational parameters","Requires tuning of learning rates and convergence criteria; no universal defaults across domains","Variational lower bound (ELBO) is not directly comparable to likelihood; harder to assess absolute model quality"],"requires":["Sparse document-term matrix representation (CSR format) for memory efficiency","Iterative optimization framework (gradient descent or coordinate ascent)","Vocabulary size typically <100K for practical convergence"],"input_types":["document-term matrix (sparse or dense)","topic count K","variational hyperparameters (learning rate, batch size, convergence threshold)"],"output_types":["variational parameters: Dirichlet shape parameters for document-topic and topic-word distributions","ELBO (evidence lower bound) trace for convergence monitoring","approximate posterior topic distributions per document"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-latent-dirichlet-allocation-lda__cap_2","uri":"capability://data.processing.analysis.interpretable.topic.word.ranking.and.visualization","name":"interpretable-topic-word-ranking-and-visualization","description":"Extracts and ranks the most probable words per topic from learned topic-word distributions, enabling human-interpretable topic summaries. Supports multiple ranking schemes (probability, lift, relevance) and integrates with visualization tools to display topic-document relationships as 2D projections, word clouds, or hierarchical dendrograms for exploratory analysis and model validation.","intents":["I want to understand what each discovered topic represents by seeing its top words","I need to validate that my topic model learned meaningful semantic clusters, not noise","I want to visualize how topics are distributed across documents and how topics relate to each other"],"best_for":["domain experts validating topic model quality before deployment","business analysts presenting findings to non-technical stakeholders","researchers exploring document collections interactively"],"limitations":["Top-word lists can be misleading if topics are dominated by common words despite stopword removal; requires domain expertise to interpret","2D projections (t-SNE, PCA) of high-dimensional topic spaces lose information; topic relationships may be misrepresented","Word rankings are marginal probabilities; don't capture word-word correlations within topics or polysemy","Visualization scalability degrades with K>50 topics; becomes cluttered and hard to interpret"],"requires":["Fitted LDA model with learned topic-word distributions","Vocabulary mapping (integer ID → word string)","Optional: visualization library (matplotlib, plotly, pyLDAvis)"],"input_types":["topic-word distribution matrix (K × V)","document-topic distribution matrix (D × K)","ranking metric (probability, lift, relevance)","number of top words to display per topic"],"output_types":["ranked word lists per topic (text or JSON)","2D/3D topic projections (coordinates for visualization)","topic similarity matrix (pairwise distances)","interactive visualizations (HTML, SVG)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-latent-dirichlet-allocation-lda__cap_3","uri":"capability://data.processing.analysis.online.streaming.topic.inference.for.new.documents","name":"online-streaming-topic-inference-for-new-documents","description":"Infers topic distributions for previously unseen documents using a fixed, pre-trained topic-word model without retraining. Applies variational inference or Gibbs sampling restricted to document-topic parameters only, treating the learned topic-word distributions as fixed. Enables real-time topic assignment for streaming documents with bounded latency and memory footprint.","intents":["I have a trained LDA model and need to assign topics to new incoming documents without retraining","I want to monitor topic evolution in a document stream (e.g., news, social media) over time","I need to classify documents into pre-discovered topics for downstream applications (search, recommendation)"],"best_for":["production systems with pre-trained topic models serving real-time inference","monitoring applications tracking topic trends in streaming data","document classification pipelines using topics as features"],"limitations":["Inference quality depends entirely on training corpus representativeness; out-of-vocabulary words are ignored or mapped to UNK token","Fixed topic-word distributions cannot adapt to domain shift or vocabulary evolution; requires periodic retraining","Inference is slower per-document than simple lookup; still requires iterative optimization (variational or sampling)","No uncertainty quantification over topic assignments in most implementations; point estimates only"],"requires":["Pre-trained LDA model (topic-word distributions and vocabulary)","New document in same format as training (tokenized, vocabulary-mapped)","Inference algorithm (variational or Gibbs sampling) with convergence criteria"],"input_types":["single document or batch of documents (token sequences)","vocabulary mapping (word string → integer ID)","inference hyperparameters (learning rate, iterations, convergence threshold)"],"output_types":["document-topic distribution (K-dimensional vector: P(topic|document))","per-token topic assignments (optional, for detailed analysis)","inference convergence metrics (ELBO or log-likelihood)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-latent-dirichlet-allocation-lda__cap_4","uri":"capability://planning.reasoning.model.selection.and.hyperparameter.optimization","name":"model-selection-and-hyperparameter-optimization","description":"Evaluates topic model quality across different topic counts K and hyperparameter settings using principled metrics: perplexity on held-out test documents, coherence scores (measuring semantic consistency of top words), and ELBO/likelihood traces. Supports grid search or Bayesian optimization over K, Dirichlet priors (α, β), and inference hyperparameters to identify configurations that balance interpretability and predictive performance.","intents":["I don't know how many topics my corpus contains; I need to find the optimal K","I want to compare different LDA configurations and select the best one objectively","I need to tune hyperparameters (α, β) to improve topic quality without manual trial-and-error"],"best_for":["researchers systematically exploring topic model design space","practitioners deploying LDA in production and needing principled model selection","teams comparing multiple topic modeling approaches"],"limitations":["Perplexity is expensive to compute (requires inference on held-out documents); limits search space size","Coherence scores correlate imperfectly with human judgment of topic quality; domain-dependent","No single metric captures all aspects of model quality; requires multi-objective optimization or manual weighting","Computational cost grows linearly with K and search space size; grid search over K=5..50 with 10-fold CV can take hours","Optimal K is often ambiguous (multiple local optima); no principled stopping criterion"],"requires":["Training corpus split into train/validation/test sets (typically 70/10/20)","Evaluation metrics implementation (perplexity, coherence, ELBO)","Computational budget for multiple model fits (hours to days for large corpora)"],"input_types":["training document collection","held-out test documents","hyperparameter search space (K range, α/β ranges, learning rates)","evaluation metric weights (if multi-objective)"],"output_types":["perplexity scores per K and hyperparameter setting","coherence scores (C_v, U_Mass, or other metrics)","ELBO/likelihood curves for convergence analysis","ranked configurations with scores","optimal hyperparameter recommendations"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-latent-dirichlet-allocation-lda__cap_5","uri":"capability://data.processing.analysis.hierarchical.topic.modeling.with.nested.structure","name":"hierarchical-topic-modeling-with-nested-structure","description":"Extends LDA to discover hierarchical topic structures where topics are organized in a tree, with parent topics representing broad themes and child topics representing specific subtopics. Implements hierarchical Dirichlet processes or nested Chinese restaurant processes to infer tree structure from data, enabling multi-level topic discovery without specifying tree depth in advance.","intents":["I want to discover topics at multiple levels of granularity (e.g., broad categories and specific subtopics)","I need to understand how topics relate hierarchically (parent-child relationships) in my document collection","I want automatic topic hierarchy discovery without manually specifying the number of levels"],"best_for":["large document collections with natural hierarchical structure (e.g., scientific papers, product catalogs, news archives)","applications requiring multi-level topic browsing or navigation","researchers studying topic evolution and specialization"],"limitations":["Inference is significantly more complex than flat LDA; requires sophisticated sampling algorithms (nested CRP sampling)","Tree structure is not unique; multiple hierarchies may explain data equally well; no principled way to select among them","Computational cost grows exponentially with tree depth; practical limit ~3-4 levels","Interpretability decreases at deeper levels; leaf topics often become noisy or document-specific","Requires more data than flat LDA to reliably infer tree structure; sparse documents lead to degenerate trees"],"requires":["Large document collection (minimum 10K+ documents for meaningful hierarchy)","Hierarchical Dirichlet process or nested CRP implementation (complex; few libraries available)","Significant computational resources (hours to days for inference)"],"input_types":["document collection (token sequences)","vocabulary mapping","hyperparameters: Dirichlet priors, concentration parameters for CRP"],"output_types":["topic hierarchy (tree structure with topic-word distributions at each node)","document-topic assignments at each hierarchy level","topic-word distributions per node","tree depth and branching factor statistics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-latent-dirichlet-allocation-lda__cap_6","uri":"capability://data.processing.analysis.dynamic.topic.modeling.with.temporal.evolution","name":"dynamic-topic-modeling-with-temporal-evolution","description":"Models how topics evolve over time by assuming topic-word distributions change smoothly across time slices (e.g., years, months). Implements Gaussian process priors or Brownian motion assumptions on topic-word parameters, enabling tracking of topic emergence, growth, decline, and semantic drift. Infers time-indexed topic-word distributions and document-topic assignments across temporal segments.","intents":["I want to track how topics and their meanings change over time in a document collection","I need to identify when new topics emerge and when old topics become obsolete","I want to understand semantic drift: how the meaning of a topic (its top words) evolves"],"best_for":["historical document analysis (news archives, scientific literature, social media over years)","trend analysis and forecasting in document streams","researchers studying language evolution and cultural shifts"],"limitations":["Assumes smooth topic evolution; cannot model abrupt shifts or discontinuities","Temporal granularity must be chosen in advance; wrong granularity (too fine/coarse) degrades results","Inference is significantly more expensive than flat LDA; requires inference across all time slices jointly","Requires sufficient documents per time slice; sparse time periods lead to unreliable estimates","Topic alignment across time is non-trivial; same topic ID at t1 and t2 may represent different semantic content"],"requires":["Document collection with timestamps (or assignable to time slices)","Minimum documents per time slice (typically 100+)","Temporal granularity specification (days, months, years)","Dynamic topic modeling implementation (gensim, custom code)"],"input_types":["document collection with timestamps","time slice boundaries (e.g., yearly splits)","topic count K","Gaussian process or Brownian motion hyperparameters"],"output_types":["time-indexed topic-word distributions (T × K × V tensor)","document-topic assignments with temporal context","topic emergence/decline curves (topic prevalence over time)","semantic drift trajectories (top-word changes per topic)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-latent-dirichlet-allocation-lda__cap_7","uri":"capability://data.processing.analysis.correlated.topic.modeling.with.topic.dependencies","name":"correlated-topic-modeling-with-topic-dependencies","description":"Extends LDA to capture correlations between topics using a logistic-normal prior on document-topic distributions instead of Dirichlet. Models topic co-occurrence patterns (e.g., documents discussing 'politics' are more likely to also discuss 'economics') through a covariance matrix, enabling discovery of topic relationships and dependencies without requiring explicit specification.","intents":["I want to discover which topics tend to co-occur in documents (topic correlations)","I need to understand topic dependencies: which topics are related or frequently discussed together","I want a richer model of document-topic relationships that captures topic interactions"],"best_for":["document collections with natural topic correlations (news, scientific papers, product reviews)","applications requiring topic relationship discovery for recommendation or navigation","researchers studying topic interactions and semantic associations"],"limitations":["Logistic-normal prior is more complex than Dirichlet; inference is slower and more difficult to implement","Covariance matrix estimation requires sufficient data; sparse corpora lead to unreliable correlation estimates","Interpretation of topic correlations is non-obvious; high correlation may reflect data artifacts rather than true relationships","Computational cost increases significantly; inference time can be 2-3x slower than flat LDA","No automatic determination of which correlations are significant; requires manual thresholding"],"requires":["Large document collection (minimum 5K+ documents) for reliable correlation estimation","Correlated topic model implementation (gensim, custom code)","Computational resources for more complex inference"],"input_types":["document collection (token sequences)","vocabulary mapping","topic count K","logistic-normal hyperparameters"],"output_types":["topic-word distributions (K × V matrix)","document-topic distributions (D × K matrix)","topic correlation matrix (K × K symmetric matrix)","topic dependency graph (edges represent significant correlations)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"low","permissions":["Text corpus with minimum 100+ documents for meaningful topic discovery","Preprocessed token sequences (whitespace-separated or array format)","Computational resources: O(D*V*K) memory where D=documents, V=vocabulary size, K=topics","Python 3.6+ with NumPy/SciPy for reference implementations (gensim, scikit-learn)","Sparse document-term matrix representation (CSR format) for memory efficiency","Iterative optimization framework (gradient descent or coordinate ascent)","Vocabulary size typically <100K for practical convergence","Fitted LDA model with learned topic-word distributions","Vocabulary mapping (integer ID → word string)","Optional: visualization library (matplotlib, plotly, pyLDAvis)"],"failure_modes":["Requires manual selection of topic count K — no automatic determination; wrong K severely degrades interpretability","Bag-of-words assumption ignores word order, syntax, and semantic relationships; struggles with short documents or sparse vocabularies","Gibbs sampling convergence can be slow on very large corpora (millions of documents); variational inference trades accuracy for speed","No built-in handling of polysemy or context-dependent word meanings; each word type has single topic distribution","Requires preprocessing: tokenization, stopword removal, and vocabulary curation; sensitive to these choices","Mean-field assumption (independence between latent variables) is often violated in practice; underestimates posterior variance","Convergence to local optima is common; sensitive to initialization of variational parameters","Requires tuning of learning rates and convergence criteria; no universal defaults across domains","Variational lower bound (ELBO) is not directly comparable to likelihood; harder to assess absolute model quality","Top-word lists can be misleading if topics are dominated by common words despite stopword removal; requires domain expertise to interpret","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.31,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-05-05T11:48:05.338Z","last_scraped_at":"2026-05-03T14:00:27.894Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=latent-dirichlet-allocation-lda","compare_url":"https://unfragile.ai/compare?artifact=latent-dirichlet-allocation-lda"}},"signature":"3HYoKHucpShCDVOYigsJWzrHAEhtxH+O6kqxg0ztcLgW26a9g28271uCbAlpRGP53XGw1dr12+vuPNfQbODcBA==","signedAt":"2026-06-17T03:26:52.911Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/latent-dirichlet-allocation-lda","artifact":"https://unfragile.ai/latent-dirichlet-allocation-lda","verify":"https://unfragile.ai/api/v1/verify?slug=latent-dirichlet-allocation-lda","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}