DTM (Document Term Matrix)

A Document Term Matrix (DTM) is a matrix representation of a text corpus where each row corresponds to a document and each column corresponds to a unique term (word) in the vocabulary - with each cell holding the frequency of that term in that document.

Structure of a DTM

• Rows - individual documents
• Columns - unique terms across the entire corpus
• Cell values - how many times a term appears in a document (or a weighted value like TF-IDF)

Building a DTM in Python

from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd

docs = ["data science is fun", "python for data science"]
vectorizer = CountVectorizer()
matrix = vectorizer.fit_transform(docs)

dtm = pd.DataFrame(matrix.toarray(), columns=vectorizer.get_feature_names_out())
print(dtm)

Why the DTM Matters

The DTM is the standard input format for many text mining and machine learning techniques - it converts a collection of documents into a numeric table that algorithms like clustering, classification, and topic modeling can work with directly.