Document-term matrix

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A document-term matrix is a mathematical matrix that describes the frequency of terms that occur in each document in a collection. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. This matrix is a specific instance of a document-feature matrix where "features" may refer to other properties of a document besides terms.^[1] It is also common to encounter the transpose, or term-document matrix where documents are the columns and terms are the rows. They are useful in the field of natural language processing and computational text analysis.^[2]

While the value of the cells is commonly the raw count of a given term, there are various schemes for weighting the raw counts such as row normalizing (i.e. relative frequency/proportions) and tf-idf.

Terms are commonly single words separated by whitespace or punctuation on either side (a.k.a. unigrams). In such a case, this is also referred to as "bag of words" representation because the counts of individual words is retained, but not the order of the words in the document.