قراءة مصفوفة المستندات والمصطلحات

A document-term matrix is a grid of counts. Each row is a document and each column is a word from your vocabulary. 🧮

One row holds the full count vector for a single document, summarizing how many times each word appeared in it.

Each column tracks one word across every document, so reading down a column shows where that word shows up.

The value at row i, column j is how many times word j appeared in document i, a single count.

The matrix shape tells you how many documents and how many unique words you are working with.

Any single document uses only a few of the thousands of words, so the matrix is mostly zeros, called sparse.

Storing only non-zero values keeps huge matrices in memory. This is why scikit-learn returns a sparse format.

Pair the array with feature names to make it readable. A DataFrame turns raw counts into a clear table.

Look at a single row to see that document profile: which words it uses and how often. That row is its fingerprint.

Similar documents have similar rows. Comparing vectors lets you measure how close two texts are in content.

This matrix is the input you feed a classifier. The DTM is the features, and your labels are the targets.

In a document-term matrix, what does one cell hold?

You read the document-term matrix: rows as documents, columns as words, cells as counts, and saw why it is sparse. 🎉