DocumentationNeurondB Documentation
Topic Discovery
Overview
Discover hidden topics in text collections using topic modeling.
LDA (Latent Dirichlet Allocation)
Extract topics from documents:
LDA topic modeling
-- LDA topic modeling
SELECT lda_topic_discovery(
'documents_table',
'text_column',
10, -- number of topics
'{}'::jsonb
);Topic Assignment
Assign topics to documents:
Get topic for each document
-- Get topic for each document
SELECT id, text,
lda_get_topic(text, 'lda_model') AS topic_id,
lda_get_topic_distribution(text, 'lda_model') AS topic_probs
FROM documents;Topic Keywords
Get keywords for each topic:
Get top keywords per topic
-- Get top keywords per topic
SELECT topic_id,
lda_get_topic_keywords('lda_model', topic_id, 10) AS keywords
FROM generate_series(0, 9) AS topic_id;Learn More
For detailed documentation on topic modeling, choosing number of topics, topic interpretation, and visualization, visit: Topic Discovery Documentation
Related Topics
- Embedding Generation - Generate embeddings from text
- Clustering - Cluster documents by similarity