Bag-of-Words
Learn about bag-of-words and how to generate its representation using Python.
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Introduction
The bag-of-words (BoW) is an essential technique to represent text data in a numerical format that machine learning algorithms can understand. We normally use this technique when we’ve cleaned the text data and need to use it for machine-learning model training. It allows us to treat text data as an unordered collection of words and disregard grammar, word order, and context. As a result, we find its application in scenarios where the context or sequence of words is less important than the frequency of individual words.
Calculating BoW
Let’s consider a simple BoW calculation for a given document. Suppose we have the following document A: “I love to eat cakes. Cakes are delicious.” To perform a BoW calculation:
We first tokenize the document, which means splitting it into individual words: [“I”, “love”, “to”, “eat”, “cakes”, “Cakes”, “are”, “delicious”].
Next, we create a vector representation of the document where each element represents the count of a specific word in the document. We consider each unique word in the document and count how many times it appears. BoW vector: [1, 1, 1, 1, 2, 1, 1, 1]. In this case, the BoW vector shows that the word “I” appears once, “love” appears once, “to” appears once, “eat” appears once, “cakes” appears twice, “are” appears once, and “delicious” appears once in the document. This BoW vector representation allows us to capture the word frequencies in the document, disregarding the order or structure of the text. ...