Topic Modeler

About this tool

Topic modelling finds recurring themes across a collection of documents without labels. This tool fits a small LDA (Latent Dirichlet Allocation) model directly from the text you paste, then reports each learned topic's top words and each document's mixture of topics. It is designed for meeting notes, support tickets, survey responses, research snippets, and other short corpora where you want a quick local map of repeated themes.

The model is deterministic for a given seed. It tokenises text, lowercases words, removes optional English stopwords plus any stopwords you add, prunes short tokens, and runs collapsed Gibbs sampling over the document-word matrix. No text is uploaded and no pretrained model is downloaded.

Worked example

Paste four short documents separated by blank lines:

Butter flour sugar and oven heat make a crisp pastry.
Baking dough with butter and sugar creates a golden crust.

Compiler modules check function signatures and return types.
Type errors appear when the module function returns the wrong value.

Set topics = 2, keep the default seed, and run the report output. You should see one topic whose words lean toward baking terms and one whose words lean toward compiler/module terms, followed by a document-mixture section that shows each document's strongest topic.

Output formats

• report gives a readable summary: corpus size, effective priors, ranked topic labels, top words with weights, and document mixtures.
• json returns the full model with topics, word probabilities, document previews, and mixture weights.
• csv returns a topic-keys table followed by a document-topic matrix, matching the shape many topic-modelling CLIs produce.

Limits and edge cases

This browser-safe implementation caps the corpus at 300 documents, 25,000 kept tokens, and 20,000 vocabulary terms. Very tiny corpora can produce unstable topics; use a fixed seed and try a few topic counts before treating the result as a real pattern. For PDFs, DOCX, EPUB, HTML, or transcripts, extract text with a separate tool first and paste the plain text here.

FAQ