The Bag of Words model is a simple and widely used technique in natural language processing for representing text data (Categorical Variable).
In the BoW model, a document is represented as a multiset (or “bag”) of its words, disregarding grammar and word order but maintaining multiplicity.
For example, consider the sentence: “The cat sat on the mat.”
Its BoW representation might look like:
json
{ "The": 1, "cat": 1, "sat": 1, "on": 1, "the": 1, "mat": 1 }
Here, each unique word in the document is represented as a key, and its value is the count of occurrences of that word in the document.
The BoW model is often used as a feature representation for text data (Text Classification) in various machine learning tasks such as Document Classification, Sentiment Analysis, and Information Retrieval.