Bag-of-Words


Bag-of-Words is a technique used in Natural Language Processing (NLP) to represent text data as numerical feature vectors. It involves creating a vocabulary of unique words from the text corpus and then counting the frequency of each word in each document. The resulting feature vectors can be used for various NLP tasks such as sentiment analysis, text classification, and topic modeling. The Bag-of-Words model is simple and efficient, but it does not capture the context and order of words in the text.


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