Document-Term Matrix Builder
Paste one document per line or a JSON array of strings, choose count or binary weighting, add unigrams/bigrams/trigrams, filter by document frequency, and export a deterministic matrix as CSV, TSV, or JSON.
About this tool
A document-term matrix turns a small text corpus into rows (documents) and columns (terms). Each cell is either a count of how many times a term appears in that document or a binary 0/1 flag that says whether the term is present. This is the classic bag-of-words representation used before clustering, similarity checks, feature inspection, and text-mining experiments.
Paste one document per line, or choose JSON input when your documents contain embedded newlines. The builder tokenizes words locally, lowercases by default, can include adjacent word n-grams, filters rare terms with min_df, and caps the vocabulary with max_features. Columns are sorted by descending document frequency, then alphabetically for stable copy-paste output.
Worked example
With these three documents:
the cat sat
the dog sat
the cat chased the dog
The default CSV output starts with terms that appear in the most documents, followed by rarer terms:
document,the,cat,dog,sat,chased,__total_terms
doc_1,1,1,0,1,0,3
doc_2,1,0,1,1,0,3
doc_3,2,1,1,0,1,5
Set weighting to binary when you only need presence/absence. Set ngram_max to 2 or 3 to include phrases such as quick fox alongside individual words.
Input notes and limits
The lines format treats each nonblank line as one document. The JSON format requires a JSON array of strings, for example ["first document", "second document"]. The tool accepts up to 10,000 documents and 5,000 output columns. N-grams are limited to lengths 1 through 3 so the matrix remains browser-friendly.
FAQ
What is the difference between count and binary weighting?
count records term frequency within each document, so repeated words produce values above 1. binary records only presence, so every nonzero count becomes 1. Binary matrices are useful for set-style similarity and feature flags.
How are terms tokenized?
Runs of Unicode letters and digits are words. Apostrophes and hyphens stay inside a word when they are between word characters, so don't and state-of-the-art remain single terms. Other punctuation separates tokens.
What does min_df do?
min_df is the minimum number of documents a term must appear in to become a column. For example, min_df = 2 removes words that appear in only one document, which is a quick way to reduce noise in a larger corpus.
Why is there no TF-IDF option?
This tool focuses on transparent document-term matrices: raw counts and binary presence. TF-IDF changes the scale and interpretation of every cell, so it belongs in a separate weighting-oriented text-vectorizer tool.
Developer & Automation Access
Run it from the terminal
Same engine as this page, headless — via the gizza CLI:
gizza tool document-term-matrix "the cat sat
the dog sat
the cat chased the dog"New to the CLI? Get gizza →
Open it by URL
Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:
https://gizza.ai/tools/document-term-matrix/?documents=the%20cat%20sat%0Athe%20dog%20sat%0Athe%20cat%20chased%20the%20dog&input_format=auto&weighting=count&case_sensitive=true&ngram_min=1&ngram_max=1&min_df=1&max_features=0&output=csv&include_totals=trueMachine-readable descriptor: tool.json — title + parameters JSON Schema for agents.