is a technique used in natural language processing to represent text data numerically. It involves converting a collection of text documents into a matrix, where each row corresponds to a document and each column corresponds to a unique word in the entire corpus.
The values in the matrix represent the frequency of each word’s occurrence in the corresponding document. Therefore, each document is represented as a vector of word counts.
For example, consider the following two documents:
- “The cat sat on the mat.”
- “The dog chased the cat.”
With count vectorization, these documents might be represented as:
| cat | chased | dog | mat | on | sat | the | |
|---|---|---|---|---|---|---|---|
| Document 1 | 1 | 0 | 0 | 1 | 1 | 1 | 2 |
| Document 2 | 1 | 1 | 1 | 0 | 0 | 0 | 2 |
Count vectorization is a simple and effective way to convert text data into a format that can be used for machine learning algorithms, such as Classification and Clustering.
Cons: It does not consider the semantic meaning of words or their order in the document.