Bag of words
Representing a document by how often it uses each vocabulary word, ignoring word order.
A model needs numbers, so the first step is to turn each document into a vector. The simplest way is a bag of words: build a vocabulary of every word seen, then represent a document by how many times it uses each one. Word order is thrown away, which sounds drastic but keeps enough to tell a delivery problem from a sizing question.
Try it now
Think about volume, cost per document, and whether the job needs understanding or just keywords.
Learn it properly
Text mining: TF-IDF, search and labels · Bag of words and TF-IDF, similarity search and its blind spots, text classifiers and topics, and when LLM labels are worth it. About 35 minutes.
Other terms from the same lesson
- TF-IDF: Term frequency times inverse document frequency: a word's weight in a document, high when it's frequent there but rare elsewhere.
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