Document-term matrix

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Definition and Components of Document-Term Matrix
– A document-term matrix is a mathematical matrix that describes the frequency of terms in a collection of documents.
– Rows in the matrix represent documents, while columns represent terms.
– It is a specific instance of a document-feature matrix, where features can refer to properties other than terms.
– The transpose of a document-term matrix is a term-document matrix, where terms are the rows and documents are the columns.
– Document-term matrices are commonly used in natural language processing and computational text analysis.

Counting and Weighting in Document-Term Matrix
– The cells in a document-term matrix typically represent the raw count of a term in a document.
– Different weighting schemes can be applied to the raw counts, such as row normalizing and tf-idf.
– Row normalizing involves dividing the counts by the total number of tokens in a document.
– Tf-idf (term frequency-inverse document frequency) is a popular weighting scheme that considers the term frequency and its document frequency.
– Document-term matrices often include all terms in the corpus, resulting in zero-counts for terms not present in specific documents.

History and Development of Document-Term Matrix
– The concept of a document-term matrix emerged in the early years of computerized text processing.
– Harold Borko published one of the first document-term matrices in 1962.
Gerard Salton also contributed to the development of document-term matrices in 1963.
– F.W. Lancaster published a comprehensive review of automated indexing and retrieval, including the document-term matrix, in 1964.
– These early works laid the foundation for the use of document-term matrices in information retrieval and text analysis.

Choosing Terms for Document-Term Matrix
– In the vectorial semantic model, each row in the document-term matrix represents a document.
– The goal is to represent the document’s topic using semantically significant terms.
– Nouns, verbs, and adjectives are often considered the most significant categories for terms in Indo-European languages.
– Adding collocations as terms can improve the quality of the document vectors and similarity computations.
– The choice of terms in a document-term matrix depends on the specific application and language characteristics.

Applications of Document-Term Matrix
– Document-term matrices are widely used in text mining, information retrieval, and text classification.
– They are essential for tasks such as document clustering and topic modeling.
– Sentiment analysis and opinion mining can also benefit from document-term matrices.
– Document-term matrices are utilized in recommendation systems and personalized content delivery.
– They provide valuable insights into the structure and content of large document collections.

Note: The subtopics and specific software mentioned in the content have not been included in the groups as they are not identical concepts.

A document-term matrix is a mathematical matrix that describes the frequency of terms that occur in a 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. 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.

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.

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