Seeded LDA

The core mechanism of Seeded LDA is the replacement of symmetric Dirichlet priors with asymmetric Beta hyperparameters. Instead of a uniform prior over the vocabulary, seed words are assigned a high prior probability, forcing the Gibbs sampling or Variational Inference process to concentrate the topic-word distribution on the specified seed terms. This effectively anchors a latent topic to a known semantic domain before the model sees the data.

Users provide a list of seed words that define the target concept. For example, a topic for 'Machine Learning' might be seeded with ['neural', 'network', 'gradient', 'backpropagation', 'loss']. During inference, the document-topic distribution is biased so that documents containing these seeds have a higher probability of being assigned to the corresponding seeded topic, ensuring the resulting topic-word distribution is interpretable and aligned with domain expertise.

Seeded LDA operates in a semi-supervised fashion. A subset of the Number of Topics (K) is anchored by seed words, while the remaining topics remain unconstrained. This allows the model to simultaneously discover known regulatory themes and novel, emergent topics from the corpus. The alpha hyperparameter can also be adjusted to control the sparsity of the document-topic mixture for both seeded and unseeded topics.

By guiding the model away from noisy, incoherent word groupings, Seeded LDA dramatically improves Topic Coherence metrics like C_V Coherence and Pointwise Mutual Information (PMI). Because the salient terms are forced to cluster around meaningful seed words, the resulting topics are more likely to pass human Topic Intrusion tests and exhibit higher semantic interpretability compared to fully unsupervised LDA.

This technique is critical for enterprise applications where generic topics are insufficient. In legal document review, seeds like ['negligence', 'liability', 'damages'] ensure the model captures the precise legal standard. In pharmacovigilance, seeds such as ['adverse', 'event', 'dose', 'reaction'] force the model to isolate safety signals from the noise of general medical text, aligning the output with a specific ontology.

The Gensim library supports Seeded LDA through the eta parameter in its LdaModel class. Instead of a scalar, eta accepts a matrix where rows correspond to topics and columns to vocabulary words. By assigning high values to the matrix cells intersecting a seed topic and its seed words, the Expectation-Maximization Algorithm (EM) or online variational Bayes is biased to converge on the guided solution, making it accessible for rapid prototyping.

Enterprises deploy Seeded LDA to enforce a consistent taxonomy on customer support tickets and product reviews. By seeding topics with domain-specific terms like 'shipping delay', 'billing error', or 'defective hardware', the model bypasses statistical noise to directly quantify the prevalence of known failure modes.

• Seeds are derived from existing CRM category tags
• Output is a document-topic distribution measuring exact issue frequency
• Benefit: Eliminates the manual relabeling of clusters that occurs with unsupervised LDA

Legal teams use Seeded LDA to isolate documents responsive to specific regulatory requests. Rather than relying on Boolean keyword searches that miss conceptual synonyms, seed words representing a legal theory or fraudulent scheme guide the model to retrieve thematically relevant documents even when exact terminology varies.

• Seeds are drafted by subject-matter experts based on the indictment
• Model surfaces documents discussing the seeded concept regardless of phrasing
• Benefit: Dramatically increases recall while reducing manual review costs

Computational social scientists seed topics with policy-specific terminology to track how media outlets frame legislative issues. By anchoring topics to known political frames, researchers measure media bias not through sentiment but through the statistical overrepresentation of specific thematic constructs.

• Seeds represent distinct ideological frames on a single issue
• Cross-outlet comparison reveals systematic framing differences
• Benefit: Provides empirical evidence of media slant grounded in topic prevalence

Pharmaceutical companies apply Seeded LDA to electronic health records to identify patient cohorts matching complex inclusion criteria. Seed terms derived from phenotype definitions and diagnostic codes guide the model to discover latent patient subgroups that share undocumented clinical characteristics.

• Seeds are curated from medical ontologies like SNOMED CT
• Model discovers comorbid conditions not explicitly coded in structured fields
• Benefit: Accelerates recruitment by finding eligible patients missed by structured queries

Market intelligence platforms use Seeded LDA to track brand associations across social media. Seeds representing brand attributes and competitor names force the model to disentangle closely related brand perceptions that unsupervised LDA would conflate into a single topic.

• Seeds are defined from brand positioning documents
• Time-series analysis detects shifts in brand association following campaigns
• Benefit: Measures marketing effectiveness through statistically grounded topic prevalence rather than simple keyword counting